Non-Disruptively Moving A Storage Fleet Control Plane

ABSTRACT

Non-disruptively moving a storage fleet control plane, including deploying, on an edge device, one or more agents that are managed by a control plane residing in a cloud computing environment; mediating one or more API requests, generated by the control plane, directed to the one or more agents on the edge device; and migrating, in response to a first condition, the control plane to the edge device.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a continuation in-part application for patent entitled to afiling date and claiming the benefit of earlier-filed U.S. patentapplication Ser. No. 17/214,426, filed Mar. 26, 2021, hereinincorporated by reference in its entirety, which claims the benefit ofU.S. Provisional Patent Application No. 63/021,835, filed May 8, 2020;this application also claims priority from U.S. Provisional PatentApplication No. 63/346,715, filed May 27, 2022.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1A illustrates a first example system for data storage inaccordance with some implementations.

FIG. 1B illustrates a second example system for data storage inaccordance with some implementations.

FIG. 1C illustrates a third example system for data storage inaccordance with some implementations.

FIG. 1D illustrates a fourth example system for data storage inaccordance with some implementations.

FIG. 2A is a perspective view of a storage cluster with multiple storagenodes and internal storage coupled to each storage node to providenetwork attached storage, in accordance with some embodiments.

FIG. 2B is a block diagram showing an interconnect switch couplingmultiple storage nodes in accordance with some embodiments.

FIG. 2C is a multiple level block diagram, showing contents of a storagenode and contents of one of the non-volatile solid state storage unitsin accordance with some embodiments.

FIG. 2D shows a storage server environment, which uses embodiments ofthe storage nodes and storage units of some previous figures inaccordance with some embodiments.

FIG. 2E is a blade hardware block diagram, showing a control plane,compute and storage planes, and authorities interacting with underlyingphysical resources, in accordance with some embodiments.

FIG. 2F depicts elasticity software layers in blades of a storagecluster, in accordance with some embodiments.

FIG. 2G depicts authorities and storage resources in blades of a storagecluster, in accordance with some embodiments.

FIG. 3A sets forth a diagram of a storage system that is coupled fordata communications with a cloud services provider in accordance withsome embodiments of the present disclosure.

FIG. 3B sets forth a diagram of a storage system in accordance with someembodiments of the present disclosure.

FIG. 3C sets forth an example of a cloud-based storage system inaccordance with some embodiments of the present disclosure.

FIG. 3D illustrates an exemplary computing device that may bespecifically configured to perform one or more of the processesdescribed herein.

FIG. 4 sets forth a flow chart illustrating an example method ofproviding data management as-a-service in accordance with someembodiments of the present disclosure.

FIG. 5 sets forth a flow chart illustrating an additional example methodof providing data management as-a-service in accordance with someembodiments of the present disclosure.

FIG. 6 sets forth a flow chart illustrating an additional example methodof providing data management as-a-service in accordance with someembodiments of the present disclosure.

FIG. 7 sets forth a diagram illustrating an example system fornon-disruptively moving a storage fleet control plane in accordance withsome embodiments of the present disclosure.

FIG. 8 sets forth a diagram illustrating an additional example systemfor non-disruptively moving a storage fleet control plane in accordancewith some embodiments of the present disclosure.

FIG. 9 sets forth a flow chart illustrating an example method ofnon-disruptively moving a storage fleet control plane in accordance withsome embodiments of the present disclosure.

FIG. 10 sets forth a flow chart illustrating an additional examplemethod of non-disruptively moving a storage fleet control plane inaccordance with some embodiments of the present disclosure.

FIG. 11 sets forth a flow chart illustrating an additional examplemethod of non-disruptively moving a storage fleet control plane inaccordance with some embodiments of the present disclosure.

FIG. 12 sets forth a flow chart illustrating an additional examplemethod of non-disruptively moving a storage fleet control plane inaccordance with some embodiments of the present disclosure.

DESCRIPTION OF EMBODIMENTS

Example methods, apparatus, and products for non-disruptively moving astorage fleet control plane in accordance with embodiments of thepresent disclosure are described with reference to the accompanyingdrawings, beginning with FIG. 1A. FIG. 1A illustrates an example systemfor data storage, in accordance with some implementations. System 100(also referred to as “storage system” herein) includes numerous elementsfor purposes of illustration rather than limitation. It may be notedthat system 100 may include the same, more, or fewer elements configuredin the same or different manner in other implementations.

System 100 includes a number of computing devices 164A-B. Computingdevices (also referred to as “client devices” herein) may be embodied,for example, a server in a data center, a workstation, a personalcomputer, a notebook, or the like. Computing devices 164A-B may becoupled for data communications to one or more storage arrays 102A-Bthrough a storage area network (‘SAN’) 158 or a local area network(‘LAN’) 160.

The SAN 158 may be implemented with a variety of data communicationsfabrics, devices, and protocols. For example, the fabrics for SAN 158may include Fibre Channel, Ethernet, Infiniband, Serial Attached SmallComputer System Interface (‘SAS’), or the like. Data communicationsprotocols for use with SAN 158 may include Advanced TechnologyAttachment (‘ATA’), Fibre Channel Protocol, Small Computer SystemInterface (‘SCSI’), Internet Small Computer System Interface (‘iSCSI’),HyperSCSI, Non-Volatile Memory Express (‘NVMe’) over Fabrics, or thelike. It may be noted that SAN 158 is provided for illustration, ratherthan limitation. Other data communication couplings may be implementedbetween computing devices 164A-B and storage arrays 102A-B.

The LAN 160 may also be implemented with a variety of fabrics, devices,and protocols. For example, the fabrics for LAN 160 may include Ethernet(802.3), wireless (802.11), or the like. Data communication protocolsfor use in LAN 160 may include Transmission Control Protocol (‘TCP’),User Datagram Protocol (‘UDP’), Internet Protocol (‘IP’), HyperTextTransfer Protocol (‘HTTP’), Wireless Access Protocol (‘WAP’), HandheldDevice Transport Protocol (‘HDTP’), Session Initiation Protocol (‘SIP’),Real Time Protocol (‘RTP’), or the like.

Storage arrays 102A-B may provide persistent data storage for thecomputing devices 164A-B. Storage array 102A may be contained in achassis (not shown), and storage array 102B may be contained in anotherchassis (not shown), in implementations. Storage array 102A and 102B mayinclude one or more storage array controllers 110A-D (also referred toas “controller” herein). A storage array controller 110A-D may beembodied as a module of automated computing machinery comprisingcomputer hardware, computer software, or a combination of computerhardware and software. In some implementations, the storage arraycontrollers 110A-D may be configured to carry out various storage tasks.Storage tasks may include writing data received from the computingdevices 164A-B to storage array 102A-B, erasing data from storage array102A-B, retrieving data from storage array 102A-B and providing data tocomputing devices 164A-B, monitoring and reporting of disk utilizationand performance, performing redundancy operations, such as RedundantArray of Independent Drives (‘RAID’) or RAID-like data redundancyoperations, compressing data, encrypting data, and so forth.

Storage array controller 110A-D may be implemented in a variety of ways,including as a Field Programmable Gate Array (‘FPGA’), a ProgrammableLogic Chip (‘PLC’), an Application Specific Integrated Circuit (‘ASIC’),System-on-Chip (‘SOC’), or any computing device that includes discretecomponents such as a processing device, central processing unit,computer memory, or various adapters. Storage array controller 110A-Dmay include, for example, a data communications adapter configured tosupport communications via the SAN 158 or LAN 160. In someimplementations, storage array controller 110A-D may be independentlycoupled to the LAN 160. In implementations, storage array controller110A-D may include an I/O controller or the like that couples thestorage array controller 110A-D for data communications, through amidplane (not shown), to a persistent storage resource 170A-B (alsoreferred to as a “storage resource” herein). The persistent storageresource 170A-B main include any number of storage drives 171A-F (alsoreferred to as “storage devices” herein) and any number of non-volatileRandom Access Memory (‘NVRAM’) devices (not shown).

In some implementations, the NVRAM devices of a persistent storageresource 170A-B may be configured to receive, from the storage arraycontroller 110A-D, data to be stored in the storage drives 171A-F. Insome examples, the data may originate from computing devices 164A-B. Insome examples, writing data to the NVRAM device may be carried out morequickly than directly writing data to the storage drive 171A-F. Inimplementations, the storage array controller 110A-D may be configuredto utilize the NVRAM devices as a quickly accessible buffer for datadestined to be written to the storage drives 171A-F. Latency for writerequests using NVRAM devices as a buffer may be improved relative to asystem in which a storage array controller 110A-D writes data directlyto the storage drives 171A-F. In some implementations, the NVRAM devicesmay be implemented with computer memory in the form of high bandwidth,low latency RAM. The NVRAM device is referred to as “non-volatile”because the NVRAM device may receive or include a unique power sourcethat maintains the state of the RAM after main power loss to the NVRAMdevice. Such a power source may be a battery, one or more capacitors, orthe like. In response to a power loss, the NVRAM device may beconfigured to write the contents of the RAM to a persistent storage,such as the storage drives 171A-F.

In implementations, storage drive 171A-F may refer to any deviceconfigured to record data persistently, where “persistently” or“persistent” refers as to a device's ability to maintain recorded dataafter loss of power. In some implementations, storage drive 171A-F maycorrespond to non-disk storage media. For example, the storage drive171A-F may be one or more solid-state drives (‘SSDs’), flash memorybased storage, any type of solid-state non-volatile memory, or any othertype of non-mechanical storage device. In other implementations, storagedrive 171A-F may include mechanical or spinning hard disk, such ashard-disk drives (‘HDD’).

In some implementations, the storage array controllers 110A-D may beconfigured for offloading device management responsibilities fromstorage drive 171A-F in storage array 102A-B. For example, storage arraycontrollers 110A-D may manage control information that may describe thestate of one or more memory blocks in the storage drives 171A-F. Thecontrol information may indicate, for example, that a particular memoryblock has failed and should no longer be written to, that a particularmemory block contains boot code for a storage array controller 110A-D,the number of program-erase (‘P/E’) cycles that have been performed on aparticular memory block, the age of data stored in a particular memoryblock, the type of data that is stored in a particular memory block, andso forth. In some implementations, the control information may be storedwith an associated memory block as metadata. In other implementations,the control information for the storage drives 171A-F may be stored inone or more particular memory blocks of the storage drives 171A-F thatare selected by the storage array controller 110A-D. The selected memoryblocks may be tagged with an identifier indicating that the selectedmemory block contains control information. The identifier may beutilized by the storage array controllers 110A-D in conjunction withstorage drives 171A-F to quickly identify the memory blocks that containcontrol information. For example, the storage controllers 110A-D mayissue a command to locate memory blocks that contain controlinformation. It may be noted that control information may be so largethat parts of the control information may be stored in multiplelocations, that the control information may be stored in multiplelocations for purposes of redundancy, for example, or that the controlinformation may otherwise be distributed across multiple memory blocksin the storage drive 171A-F.

In implementations, storage array controllers 110A-D may offload devicemanagement responsibilities from storage drives 171A-F of storage array102A-B by retrieving, from the storage drives 171A-F, controlinformation describing the state of one or more memory blocks in thestorage drives 171A-F. Retrieving the control information from thestorage drives 171A-F may be carried out, for example, by the storagearray controller 110A-D querying the storage drives 171A-F for thelocation of control information for a particular storage drive 171A-F.The storage drives 171A-F may be configured to execute instructions thatenable the storage drive 171A-F to identify the location of the controlinformation. The instructions may be executed by a controller (notshown) associated with or otherwise located on the storage drive 171A-Fand may cause the storage drive 171A-F to scan a portion of each memoryblock to identify the memory blocks that store control information forthe storage drives 171A-F. The storage drives 171A-F may respond bysending a response message to the storage array controller 110A-D thatincludes the location of control information for the storage drive171A-F. Responsive to receiving the response message, storage arraycontrollers 110A-D may issue a request to read data stored at theaddress associated with the location of control information for thestorage drives 171A-F.

In other implementations, the storage array controllers 110A-D mayfurther offload device management responsibilities from storage drives171A-F by performing, in response to receiving the control information,a storage drive management operation. A storage drive managementoperation may include, for example, an operation that is typicallyperformed by the storage drive 171A-F (e.g., the controller (not shown)associated with a particular storage drive 171A-F). A storage drivemanagement operation may include, for example, ensuring that data is notwritten to failed memory blocks within the storage drive 171A-F,ensuring that data is written to memory blocks within the storage drive171A-F in such a way that adequate wear leveling is achieved, and soforth.

In implementations, storage array 102A-B may implement two or morestorage array controllers 110A-D. For example, storage array 102A mayinclude storage array controllers 110A and storage array controllers110B. At a given instance, a single storage array controller 110A-D(e.g., storage array controller 110A) of a storage system 100 may bedesignated with primary status (also referred to as “primary controller”herein), and other storage array controllers 110A-D (e.g., storage arraycontroller 110A) may be designated with secondary status (also referredto as “secondary controller” herein). The primary controller may haveparticular rights, such as permission to alter data in persistentstorage resource 170A-B (e.g., writing data to persistent storageresource 170A-B). At least some of the rights of the primary controllermay supersede the rights of the secondary controller. For instance, thesecondary controller may not have permission to alter data in persistentstorage resource 170A-B when the primary controller has the right. Thestatus of storage array controllers 110A-D may change. For example,storage array controller 110A may be designated with secondary status,and storage array controller 110B may be designated with primary status.

In some implementations, a primary controller, such as storage arraycontroller 110A, may serve as the primary controller for one or morestorage arrays 102A-B, and a second controller, such as storage arraycontroller 110B, may serve as the secondary controller for the one ormore storage arrays 102A-B. For example, storage array controller 110Amay be the primary controller for storage array 102A and storage array102B, and storage array controller 110B may be the secondary controllerfor storage array 102A and 102B. In some implementations, storage arraycontrollers 110C and 110D (also referred to as “storage processingmodules”) may neither have primary or secondary status. Storage arraycontrollers 110C and 110D, implemented as storage processing modules,may act as a communication interface between the primary and secondarycontrollers (e.g., storage array controllers 110A and 110B,respectively) and storage array 102B. For example, storage arraycontroller 110A of storage array 102A may send a write request, via SAN158, to storage array 102B. The write request may be received by bothstorage array controllers 110C and 110D of storage array 102B. Storagearray controllers 110C and 110D facilitate the communication, e.g., sendthe write request to the appropriate storage drive 171A-F. It may benoted that in some implementations storage processing modules may beused to increase the number of storage drives controlled by the primaryand secondary controllers.

In implementations, storage array controllers 110A-D are communicativelycoupled, via a midplane (not shown), to one or more storage drives171A-F and to one or more NVRAM devices (not shown) that are included aspart of a storage array 102A-B. The storage array controllers 110A-D maybe coupled to the midplane via one or more data communication links andthe midplane may be coupled to the storage drives 171A-F and the NVRAMdevices via one or more data communications links. The datacommunications links described herein are collectively illustrated bydata communications links 108A-D and may include a Peripheral ComponentInterconnect Express (‘PCIe’) bus, for example.

FIG. 1B illustrates an example system for data storage, in accordancewith some implementations. Storage array controller 101 illustrated inFIG. 1B may be similar to the storage array controllers 110A-D describedwith respect to FIG. 1A. In one example, storage array controller 101may be similar to storage array controller 110A or storage arraycontroller 110B. Storage array controller 101 includes numerous elementsfor purposes of illustration rather than limitation. It may be notedthat storage array controller 101 may include the same, more, or fewerelements configured in the same or different manner in otherimplementations. It may be noted that elements of FIG. 1A may beincluded below to help illustrate features of storage array controller101.

Storage array controller 101 may include one or more processing devices104 and random access memory (‘RAM’) 111. Processing device 104 (orcontroller 101) represents one or more general-purpose processingdevices such as a microprocessor, central processing unit, or the like.More particularly, the processing device 104 (or controller 101) may bea complex instruction set computing (‘CISC’) microprocessor, reducedinstruction set computing (‘RISC’) microprocessor, very long instructionword (‘VLIW’) microprocessor, or a processor implementing otherinstruction sets or processors implementing a combination of instructionsets. The processing device 104 (or controller 101) may also be one ormore special-purpose processing devices such as an ASIC, an FPGA, adigital signal processor (‘DSP’), network processor, or the like.

The processing device 104 may be connected to the RAM 111 via a datacommunications link 106, which may be embodied as a high speed memorybus such as a Double-Data Rate 4 (‘DDR4’) bus. Stored in RAM 111 is anoperating system 112. In some implementations, instructions 113 arestored in RAM 111. Instructions 113 may include computer programinstructions for performing operations in in a direct-mapped flashstorage system. In one embodiment, a direct-mapped flash storage systemis one that that addresses data blocks within flash drives directly andwithout an address translation performed by the storage controllers ofthe flash drives.

In implementations, storage array controller 101 includes one or morehost bus adapters 103A-C that are coupled to the processing device 104via a data communications link 105A-C. In implementations, host busadapters 103A-C may be computer hardware that connects a host system(e.g., the storage array controller) to other network and storagearrays. In some examples, host bus adapters 103A-C may be a FibreChannel adapter that enables the storage array controller 101 to connectto a SAN, an Ethernet adapter that enables the storage array controller101 to connect to a LAN, or the like. Host bus adapters 103A-C may becoupled to the processing device 104 via a data communications link105A-C such as, for example, a PCIe bus.

In implementations, storage array controller 101 may include a host busadapter 114 that is coupled to an expander 115. The expander 115 may beused to attach a host system to a larger number of storage drives. Theexpander 115 may, for example, be a SAS expander utilized to enable thehost bus adapter 114 to attach to storage drives in an implementationwhere the host bus adapter 114 is embodied as a SAS controller.

In implementations, storage array controller 101 may include a switch116 coupled to the processing device 104 via a data communications link109. The switch 116 may be a computer hardware device that can createmultiple endpoints out of a single endpoint, thereby enabling multipledevices to share a single endpoint. The switch 116 may, for example, bea PCIe switch that is coupled to a PCIe bus (e.g., data communicationslink 109) and presents multiple PCIe connection points to the midplane.

In implementations, storage array controller 101 includes a datacommunications link 107 for coupling the storage array controller 101 toother storage array controllers. In some examples, data communicationslink 107 may be a QuickPath Interconnect (QPI) interconnect.

A traditional storage system that uses traditional flash drives mayimplement a process across the flash drives that are part of thetraditional storage system. For example, a higher level process of thestorage system may initiate and control a process across the flashdrives. However, a flash drive of the traditional storage system mayinclude its own storage controller that also performs the process. Thus,for the traditional storage system, a higher level process (e.g.,initiated by the storage system) and a lower level process (e.g.,initiated by a storage controller of the storage system) may both beperformed.

To resolve various deficiencies of a traditional storage system,operations may be performed by higher level processes and not by thelower level processes. For example, the flash storage system may includeflash drives that do not include storage controllers that provide theprocess. Thus, the operating system of the flash storage system itselfmay initiate and control the process. This may be accomplished by adirect-mapped flash storage system that addresses data blocks within theflash drives directly and without an address translation performed bythe storage controllers of the flash drives.

The operating system of the flash storage system may identify andmaintain a list of allocation units across multiple flash drives of theflash storage system. The allocation units may be entire erase blocks ormultiple erase blocks. The operating system may maintain a map oraddress range that directly maps addresses to erase blocks of the flashdrives of the flash storage system.

Direct mapping to the erase blocks of the flash drives may be used torewrite data and erase data. For example, the operations may beperformed on one or more allocation units that include a first data anda second data where the first data is to be retained and the second datais no longer being used by the flash storage system. The operatingsystem may initiate the process to write the first data to new locationswithin other allocation units and erasing the second data and markingthe allocation units as being available for use for subsequent data.Thus, the process may only be performed by the higher level operatingsystem of the flash storage system without an additional lower levelprocess being performed by controllers of the flash drives.

Advantages of the process being performed only by the operating systemof the flash storage system include increased reliability of the flashdrives of the flash storage system as unnecessary or redundant writeoperations are not being performed during the process. One possiblepoint of novelty here is the concept of initiating and controlling theprocess at the operating system of the flash storage system. Inaddition, the process can be controlled by the operating system acrossmultiple flash drives. This is contrast to the process being performedby a storage controller of a flash drive.

A storage system can consist of two storage array controllers that sharea set of drives for failover purposes, or it could consist of a singlestorage array controller that provides a storage service that utilizesmultiple drives, or it could consist of a distributed network of storagearray controllers each with some number of drives or some amount ofFlash storage where the storage array controllers in the networkcollaborate to provide a complete storage service and collaborate onvarious aspects of a storage service including storage allocation andgarbage collection.

FIG. 1C illustrates a third example system 117 for data storage inaccordance with some implementations. System 117 (also referred to as“storage system” herein) includes numerous elements for purposes ofillustration rather than limitation. It may be noted that system 117 mayinclude the same, more, or fewer elements configured in the same ordifferent manner in other implementations.

In one embodiment, system 117 includes a dual Peripheral ComponentInterconnect (‘PCI’) flash storage device 118 with separatelyaddressable fast write storage. System 117 may include a storagecontroller 119. In one embodiment, storage controller 119A-D may be aCPU, ASIC, FPGA, or any other circuitry that may implement controlstructures necessary according to the present disclosure. In oneembodiment, system 117 includes flash memory devices (e.g., includingflash memory devices 120 a-n), operatively coupled to various channelsof the storage device controller 119. Flash memory devices 120 a-n, maybe presented to the controller 119A-D as an addressable collection ofFlash pages, erase blocks, and/or control elements sufficient to allowthe storage device controller 119A-D to program and retrieve variousaspects of the Flash. In one embodiment, storage device controller119A-D may perform operations on flash memory devices 120 a-n includingstoring and retrieving data content of pages, arranging and erasing anyblocks, tracking statistics related to the use and reuse of Flash memorypages, erase blocks, and cells, tracking and predicting error codes andfaults within the Flash memory, controlling voltage levels associatedwith programming and retrieving contents of Flash cells, etc.

In one embodiment, system 117 may include RAM 121 to store separatelyaddressable fast-write data. In one embodiment, RAM 121 may be one ormore separate discrete devices. In another embodiment, RAM 121 may beintegrated into storage device controller 119A-D or multiple storagedevice controllers. The RAM 121 may be utilized for other purposes aswell, such as temporary program memory for a processing device (e.g., aCPU) in the storage device controller 119.

In one embodiment, system 117 may include a stored energy device 122,such as a rechargeable battery or a capacitor. Stored energy device 122may store energy sufficient to power the storage device controller 119,some amount of the RAM (e.g., RAM 121), and some amount of Flash memory(e.g., Flash memory 120 a-120 n) for sufficient time to write thecontents of RAM to Flash memory. In one embodiment, storage devicecontroller 119A-D may write the contents of RAM to Flash Memory if thestorage device controller detects loss of external power.

In one embodiment, system 117 includes two data communications links 123a, 123 b. In one embodiment, data communications links 123 a, 123 b maybe PCI interfaces. In another embodiment, data communications links 123a, 123 b may be based on other communications standards (e.g.,HyperTransport, InfiniBand, etc.). Data communications links 123 a, 123b may be based on non-volatile memory express (‘NVMe’) or NVMe overfabrics (‘NVMf’) specifications that allow external connection to thestorage device controller 119A-D from other components in the storagesystem 117. It should be noted that data communications links may beinterchangeably referred to herein as PCI buses for convenience.

System 117 may also include an external power source (not shown), whichmay be provided over one or both data communications links 123 a, 123 b,or which may be provided separately. An alternative embodiment includesa separate Flash memory (not shown) dedicated for use in storing thecontent of RAM 121. The storage device controller 119A-D may present alogical device over a PCI bus which may include an addressablefast-write logical device, or a distinct part of the logical addressspace of the storage device 118, which may be presented as PCI memory oras persistent storage. In one embodiment, operations to store into thedevice are directed into the RAM 121. On power failure, the storagedevice controller 119A-D may write stored content associated with theaddressable fast-write logical storage to Flash memory (e.g., Flashmemory 120 a-n) for long-term persistent storage.

In one embodiment, the logical device may include some presentation ofsome or all of the content of the Flash memory devices 120 a-n, wherethat presentation allows a storage system including a storage device 118(e.g., storage system 117) to directly address Flash memory pages anddirectly reprogram erase blocks from storage system components that areexternal to the storage device through the PCI bus. The presentation mayalso allow one or more of the external components to control andretrieve other aspects of the Flash memory including some or all of:tracking statistics related to use and reuse of Flash memory pages,erase blocks, and cells across all the Flash memory devices; trackingand predicting error codes and faults within and across the Flash memorydevices; controlling voltage levels associated with programming andretrieving contents of Flash cells; etc.

In one embodiment, the stored energy device 122 may be sufficient toensure completion of in-progress operations to the Flash memory devices120 a-120 n stored energy device 122 may power storage device controller119A-D and associated Flash memory devices (e.g., 120 a-n) for thoseoperations, as well as for the storing of fast-write RAM to Flashmemory. Stored energy device 122 may be used to store accumulatedstatistics and other parameters kept and tracked by the Flash memorydevices 120 a-n and/or the storage device controller 119. Separatecapacitors or stored energy devices (such as smaller capacitors near orembedded within the Flash memory devices themselves) may be used forsome or all of the operations described herein.

Various schemes may be used to track and optimize the life span of thestored energy component, such as adjusting voltage levels over time,partially discharging the storage energy device 122 to measurecorresponding discharge characteristics, etc. If the available energydecreases over time, the effective available capacity of the addressablefast-write storage may be decreased to ensure that it can be writtensafely based on the currently available stored energy.

FIG. 1D illustrates a third example system 124 for data storage inaccordance with some implementations. In one embodiment, system 124includes storage controllers 125 a, 125 b. In one embodiment, storagecontrollers 125 a, 125 b are operatively coupled to Dual PCI storagedevices 119 a, 119 b and 119 c, 119 d, respectively. Storage controllers125 a, 125 b may be operatively coupled (e.g., via a storage network130) to some number of host computers 127 a-n.

In one embodiment, two storage controllers (e.g., 125 a and 125 b)provide storage services, such as a SCS) block storage array, a fileserver, an object server, a database or data analytics service, etc. Thestorage controllers 125 a, 125 b may provide services through somenumber of network interfaces (e.g., 126 a-d) to host computers 127 a-noutside of the storage system 124. Storage controllers 125 a, 125 b mayprovide integrated services or an application entirely within thestorage system 124, forming a converged storage and compute system. Thestorage controllers 125 a, 125 b may utilize the fast write memorywithin or across storage devices 119 a-d to journal in progressoperations to ensure the operations are not lost on a power failure,storage controller removal, storage controller or storage systemshutdown, or some fault of one or more software or hardware componentswithin the storage system 124.

In one embodiment, controllers 125 a, 125 b operate as PCI masters toone or the other PCI buses 128 a, 128 b. In another embodiment, 128 aand 128 b may be based on other communications standards (e.g.,HyperTransport, InfiniBand, etc.). Other storage system embodiments mayoperate storage controllers 125 a, 125 b as multi-masters for both PCIbuses 128 a, 128 b. Alternately, a PCI/NVMe/NVMf switchinginfrastructure or fabric may connect multiple storage controllers. Somestorage system embodiments may allow storage devices to communicate witheach other directly rather than communicating only with storagecontrollers. In one embodiment, a storage device controller 119 a may beoperable under direction from a storage controller 125 a to synthesizeand transfer data to be stored into Flash memory devices from data thathas been stored in RAM (e.g., RAM 121 of FIG. 1C). For example, arecalculated version of RAM content may be transferred after a storagecontroller has determined that an operation has fully committed acrossthe storage system, or when fast-write memory on the device has reacheda certain used capacity, or after a certain amount of time, to ensureimprove safety of the data or to release addressable fast-write capacityfor reuse. This mechanism may be used, for example, to avoid a secondtransfer over a bus (e.g., 128 a, 128 b) from the storage controllers125 a, 125 b. In one embodiment, a recalculation may include compressingdata, attaching indexing or other metadata, combining multiple datasegments together, performing erasure code calculations, etc.

In one embodiment, under direction from a storage controller 125 a, 125b, a storage device controller 119 a, 119 b may be operable to calculateand transfer data to other storage devices from data stored in RAM(e.g., RAM 121 of FIG. 1C) without involvement of the storagecontrollers 125 a, 125 b. This operation may be used to mirror datastored in one controller 125 a to another controller 125 b, or it couldbe used to offload compression, data aggregation, and/or erasure codingcalculations and transfers to storage devices to reduce load on storagecontrollers or the storage controller interface 129 a, 129 b to the PCIbus 128 a, 128 b.

A storage device controller 119A-D may include mechanisms forimplementing high availability primitives for use by other parts of astorage system external to the Dual PCI storage device 118. For example,reservation or exclusion primitives may be provided so that, in astorage system with two storage controllers providing a highly availablestorage service, one storage controller may prevent the other storagecontroller from accessing or continuing to access the storage device.This could be used, for example, in cases where one controller detectsthat the other controller is not functioning properly or where theinterconnect between the two storage controllers may itself not befunctioning properly.

In one embodiment, a storage system for use with Dual PCI direct mappedstorage devices with separately addressable fast write storage includessystems that manage erase blocks or groups of erase blocks as allocationunits for storing data on behalf of the storage service, or for storingmetadata (e.g., indexes, logs, etc.) associated with the storageservice, or for proper management of the storage system itself. Flashpages, which may be a few kilobytes in size, may be written as dataarrives or as the storage system is to persist data for long intervalsof time (e.g., above a defined threshold of time). To commit data morequickly, or to reduce the number of writes to the Flash memory devices,the storage controllers may first write data into the separatelyaddressable fast write storage on one more storage devices.

In one embodiment, the storage controllers 125 a, 125 b may initiate theuse of erase blocks within and across storage devices (e.g., 118) inaccordance with an age and expected remaining lifespan of the storagedevices, or based on other statistics. The storage controllers 125 a,125 b may initiate garbage collection and data migration data betweenstorage devices in accordance with pages that are no longer needed aswell as to manage Flash page and erase block lifespans and to manageoverall system performance.

In one embodiment, the storage system 124 may utilize mirroring and/orerasure coding schemes as part of storing data into addressable fastwrite storage and/or as part of writing data into allocation unitsassociated with erase blocks. Erasure codes may be used across storagedevices, as well as within erase blocks or allocation units, or withinand across Flash memory devices on a single storage device, to provideredundancy against single or multiple storage device failures or toprotect against internal corruptions of Flash memory pages resultingfrom Flash memory operations or from degradation of Flash memory cells.Mirroring and erasure coding at various levels may be used to recoverfrom multiple types of failures that occur separately or in combination.

The embodiments depicted with reference to FIGS. 2A-G illustrate astorage cluster that stores user data, such as user data originatingfrom one or more user or client systems or other sources external to thestorage cluster. The storage cluster distributes user data acrossstorage nodes housed within a chassis, or across multiple chassis, usingerasure coding and redundant copies of metadata. Erasure coding refersto a method of data protection or reconstruction in which data is storedacross a set of different locations, such as disks, storage nodes orgeographic locations. Flash memory is one type of solid-state memorythat may be integrated with the embodiments, although the embodimentsmay be extended to other types of solid-state memory or other storagemedium, including non-solid state memory. Control of storage locationsand workloads are distributed across the storage locations in aclustered peer-to-peer system. Tasks such as mediating communicationsbetween the various storage nodes, detecting when a storage node hasbecome unavailable, and balancing I/Os (inputs and outputs) across thevarious storage nodes, are all handled on a distributed basis. Data islaid out or distributed across multiple storage nodes in data fragmentsor stripes that support data recovery in some embodiments. Ownership ofdata can be reassigned within a cluster, independent of input and outputpatterns. This architecture described in more detail below allows astorage node in the cluster to fail, with the system remainingoperational, since the data can be reconstructed from other storagenodes and thus remain available for input and output operations. Invarious embodiments, a storage node may be referred to as a clusternode, a blade, or a server.

The storage cluster may be contained within a chassis, i.e., anenclosure housing one or more storage nodes. A mechanism to providepower to each storage node, such as a power distribution bus, and acommunication mechanism, such as a communication bus that enablescommunication between the storage nodes are included within the chassis.The storage cluster can run as an independent system in one locationaccording to some embodiments. In one embodiment, a chassis contains atleast two instances of both the power distribution and the communicationbus which may be enabled or disabled independently. The internalcommunication bus may be an Ethernet bus, however, other technologiessuch as PCIe, InfiniBand, and others, are equally suitable. The chassisprovides a port for an external communication bus for enablingcommunication between multiple chassis, directly or through a switch,and with client systems. The external communication may use a technologysuch as Ethernet, InfiniBand, Fibre Channel, etc. In some embodiments,the external communication bus uses different communication bustechnologies for inter-chassis and client communication. If a switch isdeployed within or between chassis, the switch may act as a translationbetween multiple protocols or technologies. When multiple chassis areconnected to define a storage cluster, the storage cluster may beaccessed by a client using either proprietary interfaces or standardinterfaces such as network file system (‘NFS’), common internet filesystem (‘CIFS’), small computer system interface (‘SCSI’) or hypertexttransfer protocol (‘HTTP’). Translation from the client protocol mayoccur at the switch, chassis external communication bus or within eachstorage node. In some embodiments, multiple chassis may be coupled orconnected to each other through an aggregator switch. A portion and/orall of the coupled or connected chassis may be designated as a storagecluster. As discussed above, each chassis can have multiple blades, eachblade has a media access control (‘MAC’) address, but the storagecluster is presented to an external network as having a single clusterIP address and a single MAC address in some embodiments.

Each storage node may be one or more storage servers and each storageserver is connected to one or more non-volatile solid state memoryunits, which may be referred to as storage units or storage devices. Oneembodiment includes a single storage server in each storage node andbetween one to eight non-volatile solid state memory units, however thisone example is not meant to be limiting. The storage server may includea processor, DRAM and interfaces for the internal communication bus andpower distribution for each of the power buses. Inside the storage node,the interfaces and storage unit share a communication bus, e.g., PCIExpress, in some embodiments. The non-volatile solid state memory unitsmay directly access the internal communication bus interface through astorage node communication bus, or request the storage node to accessthe bus interface. The non-volatile solid state memory unit contains anembedded CPU, solid state storage controller, and a quantity of solidstate mass storage, e.g., between 2-32 terabytes (‘TB’) in someembodiments. An embedded volatile storage medium, such as DRAM, and anenergy reserve apparatus are included in the non-volatile solid statememory unit. In some embodiments, the energy reserve apparatus is acapacitor, super-capacitor, or battery that enables transferring asubset of DRAM contents to a stable storage medium in the case of powerloss. In some embodiments, the non-volatile solid state memory unit isconstructed with a storage class memory, such as phase change ormagnetoresistive random access memory (‘MRAM’) that substitutes for DRAMand enables a reduced power hold-up apparatus.

One of many features of the storage nodes and non-volatile solid statestorage is the ability to proactively rebuild data in a storage cluster.The storage nodes and non-volatile solid state storage can determinewhen a storage node or non-volatile solid state storage in the storagecluster is unreachable, independent of whether there is an attempt toread data involving that storage node or non-volatile solid statestorage. The storage nodes and non-volatile solid state storage thencooperate to recover and rebuild the data in at least partially newlocations. This constitutes a proactive rebuild, in that the systemrebuilds data without waiting until the data is needed for a read accessinitiated from a client system employing the storage cluster. These andfurther details of the storage memory and operation thereof arediscussed below.

FIG. 2A is a perspective view of a storage cluster 161, with multiplestorage nodes 150 and internal solid-state memory coupled to eachstorage node to provide network attached storage or storage areanetwork, in accordance with some embodiments. A network attachedstorage, storage area network, or a storage cluster, or other storagememory, could include one or more storage clusters 161, each having oneor more storage nodes 150, in a flexible and reconfigurable arrangementof both the physical components and the amount of storage memoryprovided thereby. The storage cluster 161 is designed to fit in a rack,and one or more racks can be set up and populated as desired for thestorage memory. The storage cluster 161 has a chassis 138 havingmultiple slots 142. It should be appreciated that chassis 138 may bereferred to as a housing, enclosure, or rack unit. In one embodiment,the chassis 138 has fourteen slots 142, although other numbers of slotsare readily devised. For example, some embodiments have four slots,eight slots, sixteen slots, thirty-two slots, or other suitable numberof slots. Each slot 142 can accommodate one storage node 150 in someembodiments. Chassis 138 includes flaps 148 that can be utilized tomount the chassis 138 on a rack. Fans 144 provide air circulation forcooling of the storage nodes 150 and components thereof, although othercooling components could be used, or an embodiment could be devisedwithout cooling components. A switch fabric 146 couples storage nodes150 within chassis 138 together and to a network for communication tothe memory. In an embodiment depicted in herein, the slots 142 to theleft of the switch fabric 146 and fans 144 are shown occupied by storagenodes 150, while the slots 142 to the right of the switch fabric 146 andfans 144 are empty and available for insertion of storage node 150 forillustrative purposes. This configuration is one example, and one ormore storage nodes 150 could occupy the slots 142 in various furtherarrangements. The storage node arrangements need not be sequential oradjacent in some embodiments. Storage nodes 150 are hot pluggable,meaning that a storage node 150 can be inserted into a slot 142 in thechassis 138, or removed from a slot 142, without stopping or poweringdown the system. Upon insertion or removal of storage node 150 from slot142, the system automatically reconfigures in order to recognize andadapt to the change. Reconfiguration, in some embodiments, includesrestoring redundancy and/or rebalancing data or load.

Each storage node 150 can have multiple components. In the embodimentshown here, the storage node 150 includes a printed circuit board 159populated by a CPU 156, i.e., processor, a memory 154 coupled to the CPU156, and a non-volatile solid state storage 152 coupled to the CPU 156,although other mountings and/or components could be used in furtherembodiments. The memory 154 has instructions which are executed by theCPU 156 and/or data operated on by the CPU 156. As further explainedbelow, the non-volatile solid state storage 152 includes flash or, infurther embodiments, other types of solid-state memory.

Referring to FIG. 2A, storage cluster 161 is scalable, meaning thatstorage capacity with non-uniform storage sizes is readily added, asdescribed above. One or more storage nodes 150 can be plugged into orremoved from each chassis and the storage cluster self-configures insome embodiments. Plug-in storage nodes 150, whether installed in achassis as delivered or later added, can have different sizes. Forexample, in one embodiment a storage node 150 can have any multiple of 4TB, e.g., 8 TB, 12 TB, 16 TB, 32 TB, etc. In further embodiments, astorage node 150 could have any multiple of other storage amounts orcapacities. Storage capacity of each storage node 150 is broadcast, andinfluences decisions of how to stripe the data. For maximum storageefficiency, an embodiment can self-configure as wide as possible in thestripe, subject to a predetermined requirement of continued operationwith loss of up to one, or up to two, non-volatile solid state storageunits 152 or storage nodes 150 within the chassis.

FIG. 2B is a block diagram showing a communications interconnect 173 andpower distribution bus 172 coupling multiple storage nodes 150.Referring back to FIG. 2A, the communications interconnect 173 can beincluded in or implemented with the switch fabric 146 in someembodiments. Where multiple storage clusters 161 occupy a rack, thecommunications interconnect 173 can be included in or implemented with atop of rack switch, in some embodiments. As illustrated in FIG. 2B,storage cluster 161 is enclosed within a single chassis 138. Externalport 176 is coupled to storage nodes 150 through communicationsinterconnect 173, while external port 174 is coupled directly to astorage node. External power port 178 is coupled to power distributionbus 172. Storage nodes 150 may include varying amounts and differingcapacities of non-volatile solid state storage 152 as described withreference to FIG. 2A. In addition, one or more storage nodes 150 may bea compute only storage node as illustrated in FIG. 2B. Authorities 168are implemented on the non-volatile solid state storages 152, forexample as lists or other data structures stored in memory. In someembodiments the authorities are stored within the non-volatile solidstate storage 152 and supported by software executing on a controller orother processor of the non-volatile solid state storage 152. In afurther embodiment, authorities 168 are implemented on the storage nodes150, for example as lists or other data structures stored in the memory154 and supported by software executing on the CPU 156 of the storagenode 150. Authorities 168 control how and where data is stored in thenon-volatile solid state storages 152 in some embodiments. This controlassists in determining which type of erasure coding scheme is applied tothe data, and which storage nodes 150 have which portions of the data.Each authority 168 may be assigned to a non-volatile solid state storage152. Each authority may control a range of inode numbers, segmentnumbers, or other data identifiers which are assigned to data by a filesystem, by the storage nodes 150, or by the non-volatile solid statestorage 152, in various embodiments.

Every piece of data, and every piece of metadata, has redundancy in thesystem in some embodiments. In addition, every piece of data and everypiece of metadata has an owner, which may be referred to as anauthority. If that authority is unreachable, for example through failureof a storage node, there is a plan of succession for how to find thatdata or that metadata. In various embodiments, there are redundantcopies of authorities 168. Authorities 168 have a relationship tostorage nodes 150 and non-volatile solid state storage 152 in someembodiments. Each authority 168, covering a range of data segmentnumbers or other identifiers of the data, may be assigned to a specificnon-volatile solid state storage 152. In some embodiments theauthorities 168 for all of such ranges are distributed over thenon-volatile solid state storages 152 of a storage cluster. Each storagenode 150 has a network port that provides access to the non-volatilesolid state storage(s) 152 of that storage node 150. Data can be storedin a segment, which is associated with a segment number and that segmentnumber is an indirection for a configuration of a RAID (redundant arrayof independent disks) stripe in some embodiments. The assignment and useof the authorities 168 thus establishes an indirection to data.Indirection may be referred to as the ability to reference dataindirectly, in this case via an authority 168, in accordance with someembodiments. A segment identifies a set of non-volatile solid statestorage 152 and a local identifier into the set of non-volatile solidstate storage 152 that may contain data. In some embodiments, the localidentifier is an offset into the device and may be reused sequentiallyby multiple segments. In other embodiments the local identifier isunique for a specific segment and never reused. The offsets in thenon-volatile solid state storage 152 are applied to locating data forwriting to or reading from the non-volatile solid state storage 152 (inthe form of a RAID stripe). Data is striped across multiple units ofnon-volatile solid state storage 152, which may include or be differentfrom the non-volatile solid state storage 152 having the authority 168for a particular data segment.

If there is a change in where a particular segment of data is located,e.g., during a data move or a data reconstruction, the authority 168 forthat data segment should be consulted, at that non-volatile solid statestorage 152 or storage node 150 having that authority 168. In order tolocate a particular piece of data, embodiments calculate a hash valuefor a data segment or apply an inode number or a data segment number.The output of this operation points to a non-volatile solid statestorage 152 having the authority 168 for that particular piece of data.In some embodiments there are two stages to this operation. The firststage maps an entity identifier (ID), e.g., a segment number, inodenumber, or directory number to an authority identifier. This mapping mayinclude a calculation such as a hash or a bit mask. The second stage ismapping the authority identifier to a particular non-volatile solidstate storage 152, which may be done through an explicit mapping. Theoperation is repeatable, so that when the calculation is performed, theresult of the calculation repeatably and reliably points to a particularnon-volatile solid state storage 152 having that authority 168. Theoperation may include the set of reachable storage nodes as input. Ifthe set of reachable non-volatile solid state storage units changes theoptimal set changes. In some embodiments, the persisted value is thecurrent assignment (which is always true) and the calculated value isthe target assignment the cluster will attempt to reconfigure towards.This calculation may be used to determine the optimal non-volatile solidstate storage 152 for an authority in the presence of a set ofnon-volatile solid state storage 152 that are reachable and constitutethe same cluster. The calculation also determines an ordered set of peernon-volatile solid state storage 152 that will also record the authorityto non-volatile solid state storage mapping so that the authority may bedetermined even if the assigned non-volatile solid state storage isunreachable. A duplicate or substitute authority 168 may be consulted ifa specific authority 168 is unavailable in some embodiments.

With reference to FIGS. 2A and 2B, two of the many tasks of the CPU 156on a storage node 150 are to break up write data, and reassemble readdata. When the system has determined that data is to be written, theauthority 168 for that data is located as above. When the segment ID fordata is already determined the request to write is forwarded to thenon-volatile solid state storage 152 currently determined to be the hostof the authority 168 determined from the segment. The host CPU 156 ofthe storage node 150, on which the non-volatile solid state storage 152and corresponding authority 168 reside, then breaks up or shards thedata and transmits the data out to various non-volatile solid statestorage 152. The transmitted data is written as a data stripe inaccordance with an erasure coding scheme. In some embodiments, data isrequested to be pulled, and in other embodiments, data is pushed. Inreverse, when data is read, the authority 168 for the segment IDcontaining the data is located as described above. The host CPU 156 ofthe storage node 150 on which the non-volatile solid state storage 152and corresponding authority 168 reside requests the data from thenon-volatile solid state storage and corresponding storage nodes pointedto by the authority. In some embodiments the data is read from flashstorage as a data stripe. The host CPU 156 of storage node 150 thenreassembles the read data, correcting any errors (if present) accordingto the appropriate erasure coding scheme, and forwards the reassembleddata to the network. In further embodiments, some or all of these taskscan be handled in the non-volatile solid state storage 152. In someembodiments, the segment host requests the data be sent to storage node150 by requesting pages from storage and then sending the data to thestorage node making the original request.

In some systems, for example in UNIX-style file systems, data is handledwith an index node or inode, which specifies a data structure thatrepresents an object in a file system. The object could be a file or adirectory, for example. Metadata may accompany the object, as attributessuch as permission data and a creation timestamp, among otherattributes. A segment number could be assigned to all or a portion ofsuch an object in a file system. In other systems, data segments arehandled with a segment number assigned elsewhere. For purposes ofdiscussion, the unit of distribution is an entity, and an entity can bea file, a directory or a segment. That is, entities are units of data ormetadata stored by a storage system. Entities are grouped into setscalled authorities. Each authority has an authority owner, which is astorage node that has the exclusive right to update the entities in theauthority. In other words, a storage node contains the authority, andthat the authority, in turn, contains entities.

A segment is a logical container of data in accordance with someembodiments. A segment is an address space between medium address spaceand physical flash locations, i.e., the data segment number, are in thisaddress space. Segments may also contain meta-data, which enable dataredundancy to be restored (rewritten to different flash locations ordevices) without the involvement of higher level software. In oneembodiment, an internal format of a segment contains client data andmedium mappings to determine the position of that data. Each datasegment is protected, e.g., from memory and other failures, by breakingthe segment into a number of data and parity shards, where applicable.The data and parity shards are distributed, i.e., striped, acrossnon-volatile solid state storage 152 coupled to the host CPUs 156 (SeeFIGS. 2E and 2G) in accordance with an erasure coding scheme. Usage ofthe term segments refers to the container and its place in the addressspace of segments in some embodiments. Usage of the term stripe refersto the same set of shards as a segment and includes how the shards aredistributed along with redundancy or parity information in accordancewith some embodiments.

A series of address-space transformations takes place across an entirestorage system. At the top are the directory entries (file names) whichlink to an inode. Inodes point into medium address space, where data islogically stored. Medium addresses may be mapped through a series ofindirect mediums to spread the load of large files, or implement dataservices like deduplication or snapshots. Medium addresses may be mappedthrough a series of indirect mediums to spread the load of large files,or implement data services like deduplication or snapshots. Segmentaddresses are then translated into physical flash locations. Physicalflash locations have an address range bounded by the amount of flash inthe system in accordance with some embodiments. Medium addresses andsegment addresses are logical containers, and in some embodiments use a128 bit or larger identifier so as to be practically infinite, with alikelihood of reuse calculated as longer than the expected life of thesystem. Addresses from logical containers are allocated in ahierarchical fashion in some embodiments. Initially, each non-volatilesolid state storage unit 152 may be assigned a range of address space.Within this assigned range, the non-volatile solid state storage 152 isable to allocate addresses without synchronization with othernon-volatile solid state storage 152.

Data and metadata is stored by a set of underlying storage layouts thatare optimized for varying workload patterns and storage devices. Theselayouts incorporate multiple redundancy schemes, compression formats andindex algorithms. Some of these layouts store information aboutauthorities and authority masters, while others store file metadata andfile data. The redundancy schemes include error correction codes thattolerate corrupted bits within a single storage device (such as a NANDflash chip), erasure codes that tolerate the failure of multiple storagenodes, and replication schemes that tolerate data center or regionalfailures. In some embodiments, low density parity check (‘LDPC’) code isused within a single storage unit. Reed-Solomon encoding is used withina storage cluster, and mirroring is used within a storage grid in someembodiments. Metadata may be stored using an ordered log structuredindex (such as a Log Structured Merge Tree), and large data may not bestored in a log structured layout.

In order to maintain consistency across multiple copies of an entity,the storage nodes agree implicitly on two things through calculations:(1) the authority that contains the entity, and (2) the storage nodethat contains the authority. The assignment of entities to authoritiescan be done by pseudo randomly assigning entities to authorities, bysplitting entities into ranges based upon an externally produced key, orby placing a single entity into each authority. Examples of pseudorandomschemes are linear hashing and the Replication Under Scalable Hashing(‘RUSH’) family of hashes, including Controlled Replication UnderScalable Hashing (‘CRUSH’). In some embodiments, pseudo-randomassignment is utilized only for assigning authorities to nodes becausethe set of nodes can change. The set of authorities cannot change so anysubjective function may be applied in these embodiments. Some placementschemes automatically place authorities on storage nodes, while otherplacement schemes rely on an explicit mapping of authorities to storagenodes. In some embodiments, a pseudorandom scheme is utilized to mapfrom each authority to a set of candidate authority owners. Apseudorandom data distribution function related to CRUSH may assignauthorities to storage nodes and create a list of where the authoritiesare assigned. Each storage node has a copy of the pseudorandom datadistribution function, and can arrive at the same calculation fordistributing, and later finding or locating an authority. Each of thepseudorandom schemes requires the reachable set of storage nodes asinput in some embodiments in order to conclude the same target nodes.Once an entity has been placed in an authority, the entity may be storedon physical devices so that no expected failure will lead to unexpecteddata loss. In some embodiments, rebalancing algorithms attempt to storethe copies of all entities within an authority in the same layout and onthe same set of machines.

Examples of expected failures include device failures, stolen machines,datacenter fires, and regional disasters, such as nuclear or geologicalevents. Different failures lead to different levels of acceptable dataloss. In some embodiments, a stolen storage node impacts neither thesecurity nor the reliability of the system, while depending on systemconfiguration, a regional event could lead to no loss of data, a fewseconds or minutes of lost updates, or even complete data loss.

In the embodiments, the placement of data for storage redundancy isindependent of the placement of authorities for data consistency. Insome embodiments, storage nodes that contain authorities do not containany persistent storage. Instead, the storage nodes are connected tonon-volatile solid state storage units that do not contain authorities.The communications interconnect between storage nodes and non-volatilesolid state storage units consists of multiple communicationtechnologies and has non-uniform performance and fault tolerancecharacteristics. In some embodiments, as mentioned above, non-volatilesolid state storage units are connected to storage nodes via PCIexpress, storage nodes are connected together within a single chassisusing Ethernet backplane, and chassis are connected together to form astorage cluster. Storage clusters are connected to clients usingEthernet or fiber channel in some embodiments. If multiple storageclusters are configured into a storage grid, the multiple storageclusters are connected using the Internet or other long-distancenetworking links, such as a “metro scale” link or private link that doesnot traverse the internet.

Authority owners have the exclusive right to modify entities, to migrateentities from one non-volatile solid state storage unit to anothernon-volatile solid state storage unit, and to add and remove copies ofentities. This allows for maintaining the redundancy of the underlyingdata. When an authority owner fails, is going to be decommissioned, oris overloaded, the authority is transferred to a new storage node.Transient failures make it non-trivial to ensure that all non-faultymachines agree upon the new authority location. The ambiguity thatarises due to transient failures can be achieved automatically by aconsensus protocol such as Paxos, hot-warm failover schemes, via manualintervention by a remote system administrator, or by a local hardwareadministrator (such as by physically removing the failed machine fromthe cluster, or pressing a button on the failed machine). In someembodiments, a consensus protocol is used, and failover is automatic. Iftoo many failures or replication events occur in too short a timeperiod, the system goes into a self-preservation mode and haltsreplication and data movement activities until an administratorintervenes in accordance with some embodiments.

As authorities are transferred between storage nodes and authorityowners update entities in their authorities, the system transfersmessages between the storage nodes and non-volatile solid state storageunits. With regard to persistent messages, messages that have differentpurposes are of different types. Depending on the type of the message,the system maintains different ordering and durability guarantees. Asthe persistent messages are being processed, the messages aretemporarily stored in multiple durable and non-durable storage hardwaretechnologies. In some embodiments, messages are stored in RAM, NVRAM andon NAND flash devices, and a variety of protocols are used in order tomake efficient use of each storage medium. Latency-sensitive clientrequests may be persisted in replicated NVRAM, and then later NAND,while background rebalancing operations are persisted directly to NAND.

Persistent messages are persistently stored prior to being transmitted.This allows the system to continue to serve client requests despitefailures and component replacement. Although many hardware componentscontain unique identifiers that are visible to system administrators,manufacturer, hardware supply chain and ongoing monitoring qualitycontrol infrastructure, applications running on top of theinfrastructure address virtualize addresses. These virtualized addressesdo not change over the lifetime of the storage system, regardless ofcomponent failures and replacements. This allows each component of thestorage system to be replaced over time without reconfiguration ordisruptions of client request processing, i.e., the system supportsnon-disruptive upgrades.

In some embodiments, the virtualized addresses are stored withsufficient redundancy. A continuous monitoring system correlateshardware and software status and the hardware identifiers. This allowsdetection and prediction of failures due to faulty components andmanufacturing details. The monitoring system also enables the proactivetransfer of authorities and entities away from impacted devices beforefailure occurs by removing the component from the critical path in someembodiments.

FIG. 2C is a multiple level block diagram, showing contents of a storagenode 150 and contents of a non-volatile solid state storage 152 of thestorage node 150. Data is communicated to and from the storage node 150by a network interface controller (‘NIC’) 202 in some embodiments. Eachstorage node 150 has a CPU 156, and one or more non-volatile solid statestorage 152, as discussed above. Moving down one level in FIG. 2C, eachnon-volatile solid state storage 152 has a relatively fast non-volatilesolid state memory, such as nonvolatile random access memory (‘NVRAM’)204, and flash memory 206. In some embodiments, NVRAM 204 may be acomponent that does not require program/erase cycles (DRAM, MRAM, PCM),and can be a memory that can support being written vastly more oftenthan the memory is read from. Moving down another level in FIG. 2C, theNVRAM 204 is implemented in one embodiment as high speed volatilememory, such as dynamic random access memory (DRAM) 216, backed up byenergy reserve 218. Energy reserve 218 provides sufficient electricalpower to keep the DRAM 216 powered long enough for contents to betransferred to the flash memory 206 in the event of power failure. Insome embodiments, energy reserve 218 is a capacitor, super-capacitor,battery, or other device, that supplies a suitable supply of energysufficient to enable the transfer of the contents of DRAM 216 to astable storage medium in the case of power loss. The flash memory 206 isimplemented as multiple flash dies 222, which may be referred to aspackages of flash dies 222 or an array of flash dies 222. It should beappreciated that the flash dies 222 could be packaged in any number ofways, with a single die per package, multiple dies per package (i.e.,multichip packages), in hybrid packages, as bare dies on a printedcircuit board or other substrate, as encapsulated dies, etc. In theembodiment shown, the non-volatile solid state storage 152 has acontroller 212 or other processor, and an input output (U/O) port 210coupled to the controller 212. U/O port 210 is coupled to the CPU 156and/or the network interface controller 202 of the flash storage node150. Flash input output (U/O) port 220 is coupled to the flash dies 222,and a direct memory access unit (DMA) 214 is coupled to the controller212, the DRAM 216 and the flash dies 222. In the embodiment shown, theU/O port 210, controller 212, DMA unit 214 and flash 1/O port 220 areimplemented on a programmable logic device (‘PLD’) 208, e.g., an FPGA.In this embodiment, each flash die 222 has pages, organized as sixteenkB (kilobyte) pages 224, and a register 226 through which data can bewritten to or read from the flash die 222. In further embodiments, othertypes of solid-state memory are used in place of, or in addition toflash memory illustrated within flash die 222.

Storage clusters 161, in various embodiments as disclosed herein, can becontrasted with storage arrays in general. The storage nodes 150 arepart of a collection that creates the storage cluster 161. Each storagenode 150 owns a slice of data and computing required to provide thedata. Multiple storage nodes 150 cooperate to store and retrieve thedata. Storage memory or storage devices, as used in storage arrays ingeneral, are less involved with processing and manipulating the data.Storage memory or storage devices in a storage array receive commands toread, write, or erase data. The storage memory or storage devices in astorage array are not aware of a larger system in which they areembedded, or what the data means. Storage memory or storage devices instorage arrays can include various types of storage memory, such as RAM,solid state drives, hard disk drives, etc. The storage units 152described herein have multiple interfaces active simultaneously andserving multiple purposes. In some embodiments, some of thefunctionality of a storage node 150 is shifted into a storage unit 152,transforming the storage unit 152 into a combination of storage unit 152and storage node 150. Placing computing (relative to storage data) intothe storage unit 152 places this computing closer to the data itself.The various system embodiments have a hierarchy of storage node layerswith different capabilities. By contrast, in a storage array, acontroller owns and knows everything about all of the data that thecontroller manages in a shelf or storage devices. In a storage cluster161, as described herein, multiple controllers in multiple storage units152 and/or storage nodes 150 cooperate in various ways (e.g., forerasure coding, data sharding, metadata communication and redundancy,storage capacity expansion or contraction, data recovery, and so on).

FIG. 2D shows a storage server environment, which uses embodiments ofthe storage nodes 150 and storage units 152 of FIGS. 2A-C. In thisversion, each storage unit 152 has a processor such as controller 212(see FIG. 2C), an FPGA, flash memory 206, and NVRAM 204 (which issuper-capacitor backed DRAM 216, see FIGS. 2B and 2C) on a PCIe(peripheral component interconnect express) board in a chassis 138 (seeFIG. 2A). The storage unit 152 may be implemented as a single boardcontaining storage, and may be the largest tolerable failure domaininside the chassis. In some embodiments, up to two storage units 152 mayfail and the device will continue with no data loss.

The physical storage is divided into named regions based on applicationusage in some embodiments. The NVRAM 204 is a contiguous block ofreserved memory in the storage unit 152 DRAM 216, and is backed by NANDflash. NVRAM 204 is logically divided into multiple memory regionswritten for two as spool (e.g., spool_region). Space within the NVRAM204 spools is managed by each authority 168 independently. Each deviceprovides an amount of storage space to each authority 168. Thatauthority 168 further manages lifetimes and allocations within thatspace. Examples of a spool include distributed transactions or notions.When the primary power to a storage unit 152 fails, onboardsuper-capacitors provide a short duration of power hold up. During thisholdup interval, the contents of the NVRAM 204 are flushed to flashmemory 206. On the next power-on, the contents of the NVRAM 204 arerecovered from the flash memory 206.

As for the storage unit controller, the responsibility of the logical“controller” is distributed across each of the blades containingauthorities 168. This distribution of logical control is shown in FIG.2D as a host controller 242, mid-tier controller 244 and storage unitcontroller(s) 246. Management of the control plane and the storage planeare treated independently, although parts may be physically co-locatedon the same blade. Each authority 168 effectively serves as anindependent controller. Each authority 168 provides its own data andmetadata structures, its own background workers, and maintains its ownlifecycle.

FIG. 2E is a blade 252 hardware block diagram, showing a control plane254, compute and storage planes 256, 258, and authorities 168interacting with underlying physical resources, using embodiments of thestorage nodes 150 and storage units 152 of FIGS. 2A-C in the storageserver environment of FIG. 2D. The control plane 254 is partitioned intoa number of authorities 168 which can use the compute resources in thecompute plane 256 to run on any of the blades 252. The storage plane 258is partitioned into a set of devices, each of which provides access toflash 206 and NVRAM 204 resources. In one embodiment, the compute plane256 may perform the operations of a storage array controller, asdescribed herein, on one or more devices of the storage plane 258 (e.g.,a storage array).

In the compute and storage planes 256, 258 of FIG. 2E, the authorities168 interact with the underlying physical resources (i.e., devices).From the point of view of an authority 168, its resources are stripedover all of the physical devices. From the point of view of a device, itprovides resources to all authorities 168, irrespective of where theauthorities happen to run. Each authority 168 has allocated or has beenallocated one or more partitions 260 of storage memory in the storageunits 152, e.g., partitions 260 in flash memory 206 and NVRAM 204. Eachauthority 168 uses those allocated partitions 260 that belong to it, forwriting or reading user data. Authorities can be associated withdiffering amounts of physical storage of the system. For example, oneauthority 168 could have a larger number of partitions 260 or largersized partitions 260 in one or more storage units 152 than one or moreother authorities 168.

FIG. 2F depicts elasticity software layers in blades 252 of a storagecluster, in accordance with some embodiments. In the elasticitystructure, elasticity software is symmetric, i.e., each blade's computemodule 270 runs the three identical layers of processes depicted in FIG.2F. Storage managers 274 execute read and write requests from otherblades 252 for data and metadata stored in local storage unit 152 NVRAM204 and flash 206. Authorities 168 fulfill client requests by issuingthe necessary reads and writes to the blades 252 on whose storage units152 the corresponding data or metadata resides. Endpoints 272 parseclient connection requests received from switch fabric 146 supervisorysoftware, relay the client connection requests to the authorities 168responsible for fulfillment, and relay the authorities' 168 responses toclients. The symmetric three-layer structure enables the storagesystem's high degree of concurrency. Elasticity scales out efficientlyand reliably in these embodiments. In addition, elasticity implements aunique scale-out technique that balances work evenly across allresources regardless of client access pattern, and maximizes concurrencyby eliminating much of the need for inter-blade coordination thattypically occurs with conventional distributed locking.

Still referring to FIG. 2F, authorities 168 running in the computemodules 270 of a blade 252 perform the internal operations required tofulfill client requests. One feature of elasticity is that authorities168 are stateless, i.e., they cache active data and metadata in theirown blades' 252 DRAMs for fast access, but the authorities store everyupdate in their NVRAM 204 partitions on three separate blades 252 untilthe update has been written to flash 206. All the storage system writesto NVRAM 204 are in triplicate to partitions on three separate blades252 in some embodiments. With triple-mirrored NVRAM 204 and persistentstorage protected by parity and Reed-Solomon RAID checksums, the storagesystem can survive concurrent failure of two blades 252 with no loss ofdata, metadata, or access to either.

Because authorities 168 are stateless, they can migrate between blades252. Each authority 168 has a unique identifier. NVRAM 204 and flash 206partitions are associated with authorities' 168 identifiers, not withthe blades 252 on which they are running in some. Thus, when anauthority 168 migrates, the authority 168 continues to manage the samestorage partitions from its new location. When a new blade 252 isinstalled in an embodiment of the storage cluster, the systemautomatically rebalances load by: partitioning the new blade's 252storage for use by the system's authorities 168, migrating selectedauthorities 168 to the new blade 252, starting endpoints 272 on the newblade 252 and including them in the switch fabric's 146 clientconnection distribution algorithm.

From their new locations, migrated authorities 168 persist the contentsof their NVRAM 204 partitions on flash 206, process read and writerequests from other authorities 168, and fulfill the client requeststhat endpoints 272 direct to them. Similarly, if a blade 252 fails or isremoved, the system redistributes its authorities 168 among the system'sremaining blades 252. The redistributed authorities 168 continue toperform their original functions from their new locations.

FIG. 2G depicts authorities 168 and storage resources in blades 252 of astorage cluster, in accordance with some embodiments. Each authority 168is exclusively responsible for a partition of the flash 206 and NVRAM204 on each blade 252. The authority 168 manages the content andintegrity of its partitions independently of other authorities 168.Authorities 168 compress incoming data and preserve it temporarily intheir NVRAM 204 partitions, and then consolidate, RAID-protect, andpersist the data in segments of the storage in their flash 206partitions. As the authorities 168 write data to flash 206, storagemanagers 274 perform the necessary flash translation to optimize writeperformance and maximize media longevity. In the background, authorities168 “garbage collect,” or reclaim space occupied by data that clientshave made obsolete by overwriting the data. It should be appreciatedthat since authorities' 168 partitions are disjoint, there is no needfor distributed locking to execute client and writes or to performbackground functions.

The embodiments described herein may utilize various software,communication and/or networking protocols. In addition, theconfiguration of the hardware and/or software may be adjusted toaccommodate various protocols. For example, the embodiments may utilizeActive Directory, which is a database based system that providesauthentication, directory, policy, and other services in a WINDOWS™environment. In these embodiments, LDAP (Lightweight Directory AccessProtocol) is one example application protocol for querying and modifyingitems in directory service providers such as Active Directory. In someembodiments, a network lock manager (‘NLM’) is utilized as a facilitythat works in cooperation with the Network File System (‘NFS’) toprovide a System V style of advisory file and record locking over anetwork. The Server Message Block (‘SMB’) protocol, one version of whichis also known as Common Internet File System (‘CIFS’), may be integratedwith the storage systems discussed herein. SMP operates as anapplication-layer network protocol typically used for providing sharedaccess to files, printers, and serial ports and miscellaneouscommunications between nodes on a network. SMB also provides anauthenticated inter-process communication mechanism. AMAZON™ S3 (SimpleStorage Service) is a web service offered by Amazon Web Services, andthe systems described herein may interface with Amazon S3 through webservices interfaces (REST (representational state transfer), SOAP(simple object access protocol), and BitTorrent). A RESTful API(application programming interface) breaks down a transaction to createa series of small modules. Each module addresses a particular underlyingpart of the transaction. The control or permissions provided with theseembodiments, especially for object data, may include utilization of anaccess control list (‘ACL’). The ACL is a list of permissions attachedto an object and the ACL specifies which users or system processes aregranted access to objects, as well as what operations are allowed ongiven objects. The systems may utilize Internet Protocol version 6(‘IPv6’), as well as IPv4, for the communications protocol that providesan identification and location system for computers on networks androutes traffic across the Internet. The routing of packets betweennetworked systems may include Equal-cost multi-path routing (‘ECMP’),which is a routing strategy where next-hop packet forwarding to a singledestination can occur over multiple “best paths” which tie for top placein routing metric calculations. Multi-path routing can be used inconjunction with most routing protocols because it is a per-hop decisionlimited to a single router. The software may support Multi-tenancy,which is an architecture in which a single instance of a softwareapplication serves multiple customers. Each customer may be referred toas a tenant. Tenants may be given the ability to customize some parts ofthe application, but may not customize the application's code, in someembodiments. The embodiments may maintain audit logs. An audit log is adocument that records an event in a computing system. In addition todocumenting what resources were accessed, audit log entries typicallyinclude destination and source addresses, a timestamp, and user logininformation for compliance with various regulations. The embodiments maysupport various key management policies, such as encryption keyrotation. In addition, the system may support dynamic root passwords orsome variation dynamically changing passwords.

FIG. 3A sets forth a diagram of a storage system 306 that is coupled fordata communications with a cloud services provider 302 in accordancewith some embodiments of the present disclosure. Although depicted inless detail, the storage system 306 depicted in FIG. 3A may be similarto the storage systems described above with reference to FIGS. 1A-ID andFIGS. 2A-2G. In some embodiments, the storage system 306 depicted inFIG. 3A may be embodied as a storage system that includes imbalancedactive/active controllers, as a storage system that includes balancedactive/active controllers, as a storage system that includesactive/active controllers where less than all of each controller'sresources are utilized such that each controller has reserve resourcesthat may be used to support failover, as a storage system that includesfully active/active controllers, as a storage system that includesdataset-segregated controllers, as a storage system that includesdual-layer architectures with front-end controllers and back-endintegrated storage controllers, as a storage system that includesscale-out clusters of dual-controller arrays, as well as combinations ofsuch embodiments.

In the example depicted in FIG. 3A, the storage system 306 is coupled tothe cloud services provider 302 via a data communications link 304. Thedata communications link 304 may be embodied as a dedicated datacommunications link, as a data communications pathway that is providedthrough the use of one or data communications networks such as a widearea network (‘WAN’) or LAN, or as some other mechanism capable oftransporting digital information between the storage system 306 and thecloud services provider 302. Such a data communications link 304 may befully wired, fully wireless, or some aggregation of wired and wirelessdata communications pathways. In such an example, digital informationmay be exchanged between the storage system 306 and the cloud servicesprovider 302 via the data communications link 304 using one or more datacommunications protocols. For example, digital information may beexchanged between the storage system 306 and the cloud services provider302 via the data communications link 304 using the handheld devicetransfer protocol (‘HDTP’), hypertext transfer protocol (‘HTTP’),internet protocol (‘IP’), real-time transfer protocol (‘RTP’),transmission control protocol (‘TCP’), user datagram protocol (‘UDP’),wireless application protocol (‘WAP’), or other protocol.

The cloud services provider 302 depicted in FIG. 3A may be embodied, forexample, as a system and computing environment that provides a vastarray of services to users of the cloud services provider 302 throughthe sharing of computing resources via the data communications link 304.The cloud services provider 302 may provide on-demand access to a sharedpool of configurable computing resources such as computer networks,servers, storage, applications and services, and so on. The shared poolof configurable resources may be rapidly provisioned and released to auser of the cloud services provider 302 with minimal management effort.Generally, the user of the cloud services provider 302 is unaware of theexact computing resources utilized by the cloud services provider 302 toprovide the services. Although in many cases such a cloud servicesprovider 302 may be accessible via the Internet, readers of skill in theart will recognize that any system that abstracts the use of sharedresources to provide services to a user through any data communicationslink may be considered a cloud services provider 302.

In the example depicted in FIG. 3A, the cloud services provider 302 maybe configured to provide a variety of services to the storage system 306and users of the storage system 306 through the implementation ofvarious service models. For example, the cloud services provider 302 maybe configured to provide services through the implementation of aninfrastructure as a service (‘IaaS’) service model, through theimplementation of a platform as a service (‘PaaS’) service model,through the implementation of a software as a service (‘SaaS’) servicemodel, through the implementation of an authentication as a service(‘AaaS’) service model, through the implementation of a storage as aservice model where the cloud services provider 302 offers access to itsstorage infrastructure for use by the storage system 306 and users ofthe storage system 306, and so on. Readers will appreciate that thecloud services provider 302 may be configured to provide additionalservices to the storage system 306 and users of the storage system 306through the implementation of additional service models, as the servicemodels described above are included only for explanatory purposes and inno way represent a limitation of the services that may be offered by thecloud services provider 302 or a limitation as to the service modelsthat may be implemented by the cloud services provider 302.

In the example depicted in FIG. 3A, the cloud services provider 302 maybe embodied, for example, as a private cloud, as a public cloud, or as acombination of a private cloud and public cloud. In an embodiment inwhich the cloud services provider 302 is embodied as a private cloud,the cloud services provider 302 may be dedicated to providing servicesto a single organization rather than providing services to multipleorganizations. In an embodiment where the cloud services provider 302 isembodied as a public cloud, the cloud services provider 302 may provideservices to multiple organizations. In still alternative embodiments,the cloud services provider 302 may be embodied as a mix of a privateand public cloud services with a hybrid cloud deployment.

Although not explicitly depicted in FIG. 3A, readers will appreciatethat a vast amount of additional hardware components and additionalsoftware components may be necessary to facilitate the delivery of cloudservices to the storage system 306 and users of the storage system 306.For example, the storage system 306 may be coupled to (or even include)a cloud storage gateway. Such a cloud storage gateway may be embodied,for example, as hardware-based or software-based appliance that islocated on premise with the storage system 306. Such a cloud storagegateway may operate as a bridge between local applications that areexecuting on the storage array 306 and remote, cloud-based storage thatis utilized by the storage array 306. Through the use of a cloud storagegateway, organizations may move primary iSCSI or NAS to the cloudservices provider 302, thereby enabling the organization to save spaceon their on-premises storage systems. Such a cloud storage gateway maybe configured to emulate a disk array, a block-based device, a fileserver, or other storage system that can translate the SCSI commands,file server commands, or other appropriate command into REST-spaceprotocols that facilitate communications with the cloud servicesprovider 302.

In order to enable the storage system 306 and users of the storagesystem 306 to make use of the services provided by the cloud servicesprovider 302, a cloud migration process may take place during whichdata, applications, or other elements from an organization's localsystems (or even from another cloud environment) are moved to the cloudservices provider 302. In order to successfully migrate data,applications, or other elements to the cloud services provider's 302environment, middleware such as a cloud migration tool may be utilizedto bridge gaps between the cloud services provider's 302 environment andan organization's environment. Such cloud migration tools may also beconfigured to address potentially high network costs and long transfertimes associated with migrating large volumes of data to the cloudservices provider 302, as well as addressing security concernsassociated with sensitive data to the cloud services provider 302 overdata communications networks. In order to further enable the storagesystem 306 and users of the storage system 306 to make use of theservices provided by the cloud services provider 302, a cloudorchestrator may also be used to arrange and coordinate automated tasksin pursuit of creating a consolidated process or workflow. Such a cloudorchestrator may perform tasks such as configuring various components,whether those components are cloud components or on-premises components,as well as managing the interconnections between such components. Thecloud orchestrator can simplify the inter-component communication andconnections to ensure that links are correctly configured andmaintained.

In the example depicted in FIG. 3A, and as described briefly above, thecloud services provider 302 may be configured to provide services to thestorage system 306 and users of the storage system 306 through the usageof a SaaS service model, eliminating the need to install and run theapplication on local computers, which may simplify maintenance andsupport of the application. Such applications may take many forms inaccordance with various embodiments of the present disclosure. Forexample, the cloud services provider 302 may be configured to provideaccess to data analytics applications to the storage system 306 andusers of the storage system 306. Such data analytics applications may beconfigured, for example, to receive vast amounts of telemetry dataphoned home by the storage system 306. Such telemetry data may describevarious operating characteristics of the storage system 306 and may beanalyzed for a vast array of purposes including, for example, todetermine the health of the storage system 306, to identify workloadsthat are executing on the storage system 306, to predict when thestorage system 306 will run out of various resources, to recommendconfiguration changes, hardware or software upgrades, workflowmigrations, or other actions that may improve the operation of thestorage system 306.

The cloud services provider 302 may also be configured to provide accessto virtualized computing environments to the storage system 306 andusers of the storage system 306. Such virtualized computing environmentsmay be embodied, for example, as a virtual machine or other virtualizedcomputer hardware platforms, virtual storage devices, virtualizedcomputer network resources, and so on. Examples of such virtualizedenvironments can include virtual machines that are created to emulate anactual computer, virtualized desktop environments that separate alogical desktop from a physical machine, virtualized file systems thatallow uniform access to different types of concrete file systems, andmany others.

For further explanation, FIG. 3B sets forth a diagram of a storagesystem 306 in accordance with some embodiments of the presentdisclosure. Although depicted in less detail, the storage system 306depicted in FIG. 3B may be similar to the storage systems describedabove with reference to FIGS. 1A-1D and FIGS. 2A-2G as the storagesystem may include many of the components described above.

The storage system 306 depicted in FIG. 3B may include a vast amount ofstorage resources 308, which may be embodied in many forms. For example,the storage resources 308 can include nano-RAM or another form ofnonvolatile random access memory that utilizes carbon nanotubesdeposited on a substrate, 3D crosspoint non-volatile memory, flashmemory including single-level cell (‘SLC’) NAND flash, multi-level cell(‘MLC’) NAND flash, triple-level cell (‘TLC’) NAND flash, quad-levelcell (‘QLC’) NAND flash, or others. Likewise, the storage resources 308may include non-volatile magnetoresistive random-access memory (‘MRAM’),including spin transfer torque (‘STT’) MRAM. The example storageresources 308 may alternatively include non-volatile phase-change memory(‘PCM’), quantum memory that allows for the storage and retrieval ofphotonic quantum information, resistive random-access memory (‘ReRAM’),storage class memory (‘SCM’), or other form of storage resources,including any combination of resources described herein. Readers willappreciate that other forms of computer memories and storage devices maybe utilized by the storage systems described above, including DRAM,SRAM, EEPROM, universal memory, and many others. The storage resources308 depicted in FIG. 3A may be embodied in a variety of form factors,including but not limited to, dual in-line memory modules (‘DIMMs’),non-volatile dual in-line memory modules (‘NVDIMMs’), M.2, U.2, andothers.

The storage resources 308 depicted in FIG. 3A may include various formsof SCM. SCM may effectively treat fast, non-volatile memory (e.g., NANDflash) as an extension of DRAM such that an entire dataset may betreated as an in-memory dataset that resides entirely in DRAM. SCM mayinclude non-volatile media such as, for example, NAND flash. Such NANDflash may be accessed utilizing NVMe that can use the PCIe bus as itstransport, providing for relatively low access latencies compared toolder protocols. In fact, the network protocols used for SSDs inall-flash arrays can include NVMe using Ethernet (ROCE, NVME TCP), FibreChannel (NVMe FC), InfiniBand (iWARP), and others that make it possibleto treat fast, non-volatile memory as an extension of DRAM. In view ofthe fact that DRAM is often byte-addressable and fast, non-volatilememory such as NAND flash is block-addressable, a controllersoftware/hardware stack may be needed to convert the block data to thebytes that are stored in the media. Examples of media and software thatmay be used as SCM can include, for example, 3D XPoint, Intel MemoryDrive Technology, Samsung's Z-SSD, and others.

The example storage system 306 depicted in FIG. 3B may implement avariety of storage architectures. For example, storage systems inaccordance with some embodiments of the present disclosure may utilizeblock storage where data is stored in blocks, and each block essentiallyacts as an individual hard drive. Storage systems in accordance withsome embodiments of the present disclosure may utilize object storage,where data is managed as objects. Each object may include the dataitself, a variable amount of metadata, and a globally unique identifier,where object storage can be implemented at multiple levels (e.g., devicelevel, system level, interface level). Storage systems in accordancewith some embodiments of the present disclosure utilize file storage inwhich data is stored in a hierarchical structure. Such data may be savedin files and folders, and presented to both the system storing it andthe system retrieving it in the same format.

The example storage system 306 depicted in FIG. 3B may be embodied as astorage system in which additional storage resources can be addedthrough the use of a scale-up model, additional storage resources can beadded through the use of a scale-out model, or through some combinationthereof. In a scale-up model, additional storage may be added by addingadditional storage devices. In a scale-out model, however, additionalstorage nodes may be added to a cluster of storage nodes, where suchstorage nodes can include additional processing resources, additionalnetworking resources, and so on.

The storage system 306 depicted in FIG. 3B also includes communicationsresources 310 that may be useful in facilitating data communicationsbetween components within the storage system 306, as well as datacommunications between the storage system 306 and computing devices thatare outside of the storage system 306, including embodiments where thoseresources are separated by a relatively vast expanse. The communicationsresources 310 may be configured to utilize a variety of differentprotocols and data communication fabrics to facilitate datacommunications between components within the storage systems as well ascomputing devices that are outside of the storage system. For example,the communications resources 310 can include fibre channel (‘FC’)technologies such as FC fabrics and FC protocols that can transport SCSIcommands over FC network, FC over ethernet (‘FCoE’) technologies throughwhich FC frames are encapsulated and transmitted over Ethernet networks,InfiniBand (‘IB’) technologies in which a switched fabric topology isutilized to facilitate transmissions between channel adapters, NVMExpress (‘NVMe’) technologies and NVMe over fabrics (‘NVMeoF’)technologies through which non-volatile storage media attached via a PCIexpress (‘PCIe’) bus may be accessed, and others. In fact, the storagesystems described above may, directly or indirectly, make use ofneutrino communication technologies and devices through whichinformation (including binary information) is transmitted using a beamof neutrinos.

The communications resources 310 can also include mechanisms foraccessing storage resources 308 within the storage system 306 utilizingserial attached SCSI (‘SAS’), serial ATA (‘SATA’) bus interfaces forconnecting storage resources 308 within the storage system 306 to hostbus adapters within the storage system 306, internet small computersystems interface (‘iSCSI’) technologies to provide block-level accessto storage resources 308 within the storage system 306, and othercommunications resources that that may be useful in facilitating datacommunications between components within the storage system 306, as wellas data communications between the storage system 306 and computingdevices that are outside of the storage system 306.

The storage system 306 depicted in FIG. 3B also includes processingresources 312 that may be useful in useful in executing computer programinstructions and performing other computational tasks within the storagesystem 306. The processing resources 312 may include one or more ASICsthat are customized for some particular purpose as well as one or moreCPUs. The processing resources 312 may also include one or more DSPs,one or more FPGAs, one or more systems on a chip (‘SoCs’), or other formof processing resources 312. The storage system 306 may utilize thestorage resources 312 to perform a variety of tasks including, but notlimited to, supporting the execution of software resources 314 that willbe described in greater detail below.

The storage system 306 depicted in FIG. 3B also includes softwareresources 314 that, when executed by processing resources 312 within thestorage system 306, may perform a vast array of tasks. The softwareresources 314 may include, for example, one or more modules of computerprogram instructions that when executed by processing resources 312within the storage system 306 are useful in carrying out various dataprotection techniques to preserve the integrity of data that is storedwithin the storage systems. Readers will appreciate that such dataprotection techniques may be carried out, for example, by systemsoftware executing on computer hardware within the storage system, by acloud services provider, or in other ways. Such data protectiontechniques can include, for example, data archiving techniques thatcause data that is no longer actively used to be moved to a separatestorage device or separate storage system for long-term retention, databackup techniques through which data stored in the storage system may becopied and stored in a distinct location to avoid data loss in the eventof equipment failure or some other form of catastrophe with the storagesystem, data replication techniques through which data stored in thestorage system is replicated to another storage system such that thedata may be accessible via multiple storage systems, data snapshottingtechniques through which the state of data within the storage system iscaptured at various points in time, data and database cloning techniquesthrough which duplicate copies of data and databases may be created, andother data protection techniques.

The software resources 314 may also include software that is useful inimplementing software-defined storage (‘SDS’). In such an example, thesoftware resources 314 may include one or more modules of computerprogram instructions that, when executed, are useful in policy-basedprovisioning and management of data storage that is independent of theunderlying hardware. Such software resources 314 may be useful inimplementing storage virtualization to separate the storage hardwarefrom the software that manages the storage hardware.

The software resources 314 may also include software that is useful infacilitating and optimizing I/O operations that are directed to thestorage resources 308 in the storage system 306. For example, thesoftware resources 314 may include software modules that perform carryout various data reduction techniques such as, for example, datacompression, data deduplication, and others. The software resources 314may include software modules that intelligently group together I/Ooperations to facilitate better usage of the underlying storage resource308, software modules that perform data migration operations to migratefrom within a storage system, as well as software modules that performother functions. Such software resources 314 may be embodied as one ormore software containers or in many other ways.

For further explanation, FIG. 3C sets forth an example of a cloud-basedstorage system 318 in accordance with some embodiments of the presentdisclosure. In the example depicted in FIG. 3C, the cloud-based storagesystem 318 is created entirely in a cloud computing environment 316 suchas, for example, Amazon Web Services (‘AWS’), Microsoft Azure, GoogleCloud Platform, IBM Cloud, Oracle Cloud, and others. The cloud-basedstorage system 318 may be used to provide services similar to theservices that may be provided by the storage systems described above.For example, the cloud-based storage system 318 may be used to provideblock storage services to users of the cloud-based storage system 318,the cloud-based storage system 318 may be used to provide storageservices to users of the cloud-based storage system 318 through the useof solid-state storage, and so on.

The cloud-based storage system 318 depicted in FIG. 3C includes twocloud computing instances 320, 322 that each are used to support theexecution of a storage controller application 324, 326. The cloudcomputing instances 320, 322 may be embodied, for example, as instancesof cloud computing resources (e.g., virtual machines) that may beprovided by the cloud computing environment 316 to support the executionof software applications such as the storage controller application 324,326. In one embodiment, the cloud computing instances 320, 322 may beembodied as Amazon Elastic Compute Cloud (‘EC2’) instances. In such anexample, an Amazon Machine Image (‘AMI’) that includes the storagecontroller application 324, 326 may be booted to create and configure avirtual machine that may execute the storage controller application 324,326.

In the example method depicted in FIG. 3C, the storage controllerapplication 324, 326 may be embodied as a module of computer programinstructions that, when executed, carries out various storage tasks. Forexample, the storage controller application 324, 326 may be embodied asa module of computer program instructions that, when executed, carriesout the same tasks as the controllers 110A, 110B in FIG. 1A describedabove such as writing data received from the users of the cloud-basedstorage system 318 to the cloud-based storage system 318, erasing datafrom the cloud-based storage system 318, retrieving data from thecloud-based storage system 318 and providing such data to users of thecloud-based storage system 318, monitoring and reporting of diskutilization and performance, performing redundancy operations, such asRAID or RAID-like data redundancy operations, compressing data,encrypting data, deduplicating data, and so forth. Readers willappreciate that because there are two cloud computing instances 320, 322that each include the storage controller application 324, 326, in someembodiments one cloud computing instance 320 may operate as the primarycontroller as described above while the other cloud computing instance322 may operate as the secondary controller as described above. Readerswill appreciate that the storage controller application 324, 326depicted in FIG. 3C may include identical source code that is executedwithin different cloud computing instances 320, 322.

Consider an example in which the cloud computing environment 316 isembodied as AWS and the cloud computing instances are embodied as EC2instances. In such an example, the cloud computing instance 320 thatoperates as the primary controller may be deployed on one of theinstance types that has a relatively large amount of memory andprocessing power while the cloud computing instance 322 that operates asthe secondary controller may be deployed on one of the instance typesthat has a relatively small amount of memory and processing power. Insuch an example, upon the occurrence of a failover event where the rolesof primary and secondary are switched, a double failover may actually becarried out such that: 1) a first failover event where the cloudcomputing instance 322 that formerly operated as the secondarycontroller begins to operate as the primary controller, and 2) a thirdcloud computing instance (not shown) that is of an instance type thathas a relatively large amount of memory and processing power is spun upwith a copy of the storage controller application, where the third cloudcomputing instance begins operating as the primary controller while thecloud computing instance 322 that originally operated as the secondarycontroller begins operating as the secondary controller again. In suchan example, the cloud computing instance 320 that formerly operated asthe primary controller may be terminated. Readers will appreciate thatin alternative embodiments, the cloud computing instance 320 that isoperating as the secondary controller after the failover event maycontinue to operate as the secondary controller and the cloud computinginstance 322 that operated as the primary controller after theoccurrence of the failover event may be terminated once the primary rolehas been assumed by the third cloud computing instance (not shown).

Readers will appreciate that while the embodiments described aboverelate to embodiments where one cloud computing instance 320 operates asthe primary controller and the second cloud computing instance 322operates as the secondary controller, other embodiments are within thescope of the present disclosure. For example, each cloud computinginstance 320, 322 may operate as a primary controller for some portionof the address space supported by the cloud-based storage system 318,each cloud computing instance 320, 322 may operate as a primarycontroller where the servicing of I/O operations directed to thecloud-based storage system 318 are divided in some other way, and so on.In fact, in other embodiments where costs savings may be prioritizedover performance demands, only a single cloud computing instance mayexist that contains the storage controller application.

The cloud-based storage system 318 depicted in FIG. 3C includes cloudcomputing instances 340 a, 340 b, 340 n with local storage 330, 334,338. The cloud computing instances 340 a, 340 b, 340 n depicted in FIG.3C may be embodied, for example, as instances of cloud computingresources that may be provided by the cloud computing environment 316 tosupport the execution of software applications. The cloud computinginstances 340 a, 340 b, 340 n of FIG. 3C may differ from the cloudcomputing instances 320, 322 described above as the cloud computinginstances 340 a, 340 b, 340 n of FIG. 3C have local storage 330, 334,338 resources whereas the cloud computing instances 320, 322 thatsupport the execution of the storage controller application 324, 326need not have local storage resources. The cloud computing instances 340a, 340 b, 340 n with local storage 330, 334, 338 may be embodied, forexample, as EC2 M5 instances that include one or more SSDs, as EC2 R5instances that include one or more SSDs, as EC2 I3 instances thatinclude one or more SSDs, and so on. In some embodiments, the localstorage 330, 334, 338 must be embodied as solid-state storage (e.g.,SSDs) rather than storage that makes use of hard disk drives.

In the example depicted in FIG. 3C, each of the cloud computinginstances 340 a, 340 b, 340 n with local storage 330, 334, 338 caninclude a software daemon 328, 332, 336 that, when executed by a cloudcomputing instance 340 a, 340 b, 340 n can present itself to the storagecontroller applications 324, 326 as if the cloud computing instance 340a, 340 b, 340 n were a physical storage device (e.g., one or more SSDs).In such an example, the software daemon 328, 332, 336 may includecomputer program instructions similar to those that would normally becontained on a storage device such that the storage controllerapplications 324, 326 can send and receive the same commands that astorage controller would send to storage devices. In such a way, thestorage controller applications 324, 326 may include code that isidentical to (or substantially identical to) the code that would beexecuted by the controllers in the storage systems described above. Inthese and similar embodiments, communications between the storagecontroller applications 324, 326 and the cloud computing instances 340a, 340 b, 340 n with local storage 330, 334, 338 may utilize iSCSI, NVMeover TCP, messaging, a custom protocol, or in some other mechanism.

In the example depicted in FIG. 3C, each of the cloud computinginstances 340 a, 340 b, 340 n with local storage 330, 334, 338 may alsobe coupled to block-storage 342, 344, 346 that is offered by the cloudcomputing environment 316. The block-storage 342, 344, 346 that isoffered by the cloud computing environment 316 may be embodied, forexample, as Amazon Elastic Block Store (‘EBS’) volumes. For example, afirst EBS volume may be coupled to a first cloud computing instance 340a, a second EBS volume may be coupled to a second cloud computinginstance 340 b, and a third EBS volume may be coupled to a third cloudcomputing instance 340 n. In such an example, the block-storage 342,344, 346 that is offered by the cloud computing environment 316 may beutilized in a manner that is similar to how the NVRAM devices describedabove are utilized, as the software daemon 328, 332, 336 (or some othermodule) that is executing within a particular cloud comping instance 340a, 340 b, 340 n may, upon receiving a request to write data, initiate awrite of the data to its attached EBS volume as well as a write of thedata to its local storage 330, 334, 338 resources. In some alternativeembodiments, data may only be written to the local storage 330, 334, 338resources within a particular cloud comping instance 340 a, 340 b, 340n. In an alternative embodiment, rather than using the block-storage342, 344, 346 that is offered by the cloud computing environment 316 asNVRAM, actual RAM on each of the cloud computing instances 340 a, 340 b,340 n with local storage 330, 334, 338 may be used as NVRAM, therebydecreasing network utilization costs that would be associated with usingan EBS volume as the NVRAM.

In the example depicted in FIG. 3C, the cloud computing instances 340 a,340 b, 340 n with local storage 330, 334, 338 may be utilized, by cloudcomputing instances 320, 322 that support the execution of the storagecontroller application 324, 326 to service I/O operations that aredirected to the cloud-based storage system 318. Consider an example inwhich a first cloud computing instance 320 that is executing the storagecontroller application 324 is operating as the primary controller. Insuch an example, the first cloud computing instance 320 that isexecuting the storage controller application 324 may receive (directlyor indirectly via the secondary controller) requests to write data tothe cloud-based storage system 318 from users of the cloud-based storagesystem 318. In such an example, the first cloud computing instance 320that is executing the storage controller application 324 may performvarious tasks such as, for example, deduplicating the data contained inthe request, compressing the data contained in the request, determiningwhere to the write the data contained in the request, and so on, beforeultimately sending a request to write a deduplicated, encrypted, orotherwise possibly updated version of the data to one or more of thecloud computing instances 340 a, 340 b, 340 n with local storage 330,334, 338. Either cloud computing instance 320, 322, in some embodiments,may receive a request to read data from the cloud-based storage system318 and may ultimately send a request to read data to one or more of thecloud computing instances 340 a, 340 b, 340 n with local storage 330,334, 338.

Readers will appreciate that when a request to write data is received bya particular cloud computing instance 340 a, 340 b, 340 n with localstorage 330, 334, 338, the software daemon 328, 332, 336 or some othermodule of computer program instructions that is executing on theparticular cloud computing instance 340 a, 340 b, 340 n may beconfigured to not only write the data to its own local storage 330, 334,338 resources and any appropriate block-storage 342, 344, 346 that areoffered by the cloud computing environment 316, but the software daemon328, 332, 336 or some other module of computer program instructions thatis executing on the particular cloud computing instance 340 a, 340 b,340 n may also be configured to write the data to cloud-based objectstorage 348 that is attached to the particular cloud computing instance340 a, 340 b, 340 n. The cloud-based object storage 348 that is attachedto the particular cloud computing instance 340 a, 340 b, 340 n may beembodied, for example, as Amazon Simple Storage Service (‘S3’) storagethat is accessible by the particular cloud computing instance 340 a, 340b, 340 n. In other embodiments, the cloud computing instances 320, 322that each include the storage controller application 324, 326 mayinitiate the storage of the data in the local storage 330, 334, 338 ofthe cloud computing instances 340 a, 340 b, 340 n and the cloud-basedobject storage 348.

Readers will appreciate that, as described above, the cloud-basedstorage system 318 may be used to provide block storage services tousers of the cloud-based storage system 318. While the local storage330, 334, 338 resources and the block-storage 342, 344, 346 resourcesthat are utilized by the cloud computing instances 340 a, 340 b, 340 nmay support block-level access, the cloud-based object storage 348 thatis attached to the particular cloud computing instance 340 a, 340 b, 340n supports only object-based access. In order to address this, thesoftware daemon 328, 332, 336 or some other module of computer programinstructions that is executing on the particular cloud computinginstance 340 a, 340 b, 340 n may be configured to take blocks of data,package those blocks into objects, and write the objects to thecloud-based object storage 348 that is attached to the particular cloudcomputing instance 340 a, 340 b, 340 n.

Consider an example in which data is written to the local storage 330,334, 338 resources and the block-storage 342, 344, 346 resources thatare utilized by the cloud computing instances 340 a, 340 b, 340 n in 1MB blocks. In such an example, assume that a user of the cloud-basedstorage system 318 issues a request to write data that, after beingcompressed and deduplicated by the storage controller application 324,326 results in the need to write 5 MB of data. In such an example,writing the data to the local storage 330, 334, 338 resources and theblock-storage 342, 344, 346 resources that are utilized by the cloudcomputing instances 340 a, 340 b, 340 n is relatively straightforward as5 blocks that are 1 MB in size are written to the local storage 330,334, 338 resources and the block-storage 342, 344, 346 resources thatare utilized by the cloud computing instances 340 a, 340 b, 340 n. Insuch an example, the software daemon 328, 332, 336 or some other moduleof computer program instructions that is executing on the particularcloud computing instance 340 a, 340 b, 340 n may be configured to: 1)create a first object that includes the first 1 MB of data and write thefirst object to the cloud-based object storage 348, 2) create a secondobject that includes the second 1 MB of data and write the second objectto the cloud-based object storage 348, 3) create a third object thatincludes the third 1 MB of data and write the third object to thecloud-based object storage 348, and so on. As such, in some embodiments,each object that is written to the cloud-based object storage 348 may beidentical (or nearly identical) in size. Readers will appreciate that insuch an example, metadata that is associated with the data itself may beincluded in each object (e.g., the first 1 MB of the object is data andthe remaining portion is metadata associated with the data).

Readers will appreciate that the cloud-based object storage 348 may beincorporated into the cloud-based storage system 318 to increase thedurability of the cloud-based storage system 318. Continuing with theexample described above where the cloud computing instances 340 a, 340b, 340 n are EC2 instances, readers will understand that EC2 instancesare only guaranteed to have a monthly uptime of 99.9% and data stored inthe local instance store only persists during the lifetime of the EC2instance. As such, relying on the cloud computing instances 340 a, 340b, 340 n with local storage 330, 334, 338 as the only source ofpersistent data storage in the cloud-based storage system 318 may resultin a relatively unreliable storage system. Likewise, EBS volumes aredesigned for 99.999% availability. As such, even relying on EBS as thepersistent data store in the cloud-based storage system 318 may resultin a storage system that is not sufficiently durable. Amazon S3,however, is designed to provide 99.999999999% durability, meaning that acloud-based storage system 318 that can incorporate S3 into its pool ofstorage is substantially more durable than various other options.

Readers will appreciate that while a cloud-based storage system 318 thatcan incorporate S3 into its pool of storage is substantially moredurable than various other options, utilizing S3 as the primary pool ofstorage may result in storage system that has relatively slow responsetimes and relatively long I/O latencies. As such, the cloud-basedstorage system 318 depicted in FIG. 3C not only stores data in S3 butthe cloud-based storage system 318 also stores data in local storage330, 334, 338 resources and block-storage 342, 344, 346 resources thatare utilized by the cloud computing instances 340 a, 340 b, 340 n, suchthat read operations can be serviced from local storage 330, 334, 338resources and the block-storage 342, 344, 346 resources that areutilized by the cloud computing instances 340 a, 340 b, 340 n, therebyreducing read latency when users of the cloud-based storage system 318attempt to read data from the cloud-based storage system 318.

In some embodiments, all data that is stored by the cloud-based storagesystem 318 may be stored in both: 1) the cloud-based object storage 348,and 2) at least one of the local storage 330, 334, 338 resources orblock-storage 342, 344, 346 resources that are utilized by the cloudcomputing instances 340 a, 340 b, 340 n. In such embodiments, the localstorage 330, 334, 338 resources and block-storage 342, 344, 346resources that are utilized by the cloud computing instances 340 a, 340b, 340 n may effectively operate as cache that generally includes alldata that is also stored in S3, such that all reads of data may beserviced by the cloud computing instances 340 a, 340 b, 340 n withoutrequiring the cloud computing instances 340 a, 340 b, 340 n to accessthe cloud-based object storage 348. Readers will appreciate that inother embodiments, however, all data that is stored by the cloud-basedstorage system 318 may be stored in the cloud-based object storage 348,but less than all data that is stored by the cloud-based storage system318 may be stored in at least one of the local storage 330, 334, 338resources or block-storage 342, 344, 346 resources that are utilized bythe cloud computing instances 340 a, 340 b, 340 n. In such an example,various policies may be utilized to determine which subset of the datathat is stored by the cloud-based storage system 318 should reside inboth: 1) the cloud-based object storage 348, and 2) at least one of thelocal storage 330, 334, 338 resources or block-storage 342, 344, 346resources that are utilized by the cloud computing instances 340 a, 340b, 340 n.

As described above, when the cloud computing instances 340 a, 340 b, 340n with local storage 330, 334, 338 are embodied as EC2 instances, thecloud computing instances 340 a, 340 b, 340 n with local storage 330,334, 338 are only guaranteed to have a monthly uptime of 99.9% and datastored in the local instance store only persists during the lifetime ofeach cloud computing instance 340 a, 340 b, 340 n with local storage330, 334, 338. As such, one or more modules of computer programinstructions that are executing within the cloud-based storage system318 (e.g., a monitoring module that is executing on its own EC2instance) may be designed to handle the failure of one or more of thecloud computing instances 340 a, 340 b, 340 n with local storage 330,334, 338. In such an example, the monitoring module may handle thefailure of one or more of the cloud computing instances 340 a, 340 b,340 n with local storage 330, 334, 338 by creating one or more new cloudcomputing instances with local storage, retrieving data that was storedon the failed cloud computing instances 340 a, 340 b, 340 n from thecloud-based object storage 348, and storing the data retrieved from thecloud-based object storage 348 in local storage on the newly createdcloud computing instances. Readers will appreciate that many variants ofthis process may be implemented.

Consider an example in which all cloud computing instances 340 a, 340 b,340 n with local storage 330, 334, 338 failed. In such an example, themonitoring module may create new cloud computing instances with localstorage, where high-bandwidth instances types are selected that allowfor the maximum data transfer rates between the newly createdhigh-bandwidth cloud computing instances with local storage and thecloud-based object storage 348. Readers will appreciate that instancestypes are selected that allow for the maximum data transfer ratesbetween the new cloud computing instances and the cloud-based objectstorage 348 such that the new high-bandwidth cloud computing instancescan be rehydrated with data from the cloud-based object storage 348 asquickly as possible. Once the new high-bandwidth cloud computinginstances are rehydrated with data from the cloud-based object storage348, less expensive lower-bandwidth cloud computing instances may becreated, data may be migrated to the less expensive lower-bandwidthcloud computing instances, and the high-bandwidth cloud computinginstances may be terminated.

Readers will appreciate that in some embodiments, the number of newcloud computing instances that are created may substantially exceed thenumber of cloud computing instances that are needed to locally store allof the data stored by the cloud-based storage system 318. The number ofnew cloud computing instances that are created may substantially exceedthe number of cloud computing instances that are needed to locally storeall of the data stored by the cloud-based storage system 318 in order tomore rapidly pull data from the cloud-based object storage 348 and intothe new cloud computing instances, as each new cloud computing instancecan (in parallel) retrieve some portion of the data stored by thecloud-based storage system 318. In such embodiments, once the datastored by the cloud-based storage system 318 has been pulled into thenewly created cloud computing instances, the data may be consolidatedwithin a subset of the newly created cloud computing instances and thosenewly created cloud computing instances that are excessive may beterminated.

Consider an example in which 1000 cloud computing instances are neededin order to locally store all valid data that users of the cloud-basedstorage system 318 have written to the cloud-based storage system 318.In such an example, assume that all 1,000 cloud computing instancesfail. In such an example, the monitoring module may cause 100,000 cloudcomputing instances to be created, where each cloud computing instanceis responsible for retrieving, from the cloud-based object storage 348,distinct 1/100,000th chunks of the valid data that users of thecloud-based storage system 318 have written to the cloud-based storagesystem 318 and locally storing the distinct chunk of the dataset that itretrieved. In such an example, because each of the 100,000 cloudcomputing instances can retrieve data from the cloud-based objectstorage 348 in parallel, the caching layer may be restored 100 timesfaster as compared to an embodiment where the monitoring module onlycreate 1000 replacement cloud computing instances. In such an example,over time the data that is stored locally in the 100,000 could beconsolidated into 1,000 cloud computing instances and the remaining99,000 cloud computing instances could be terminated.

Readers will appreciate that various performance aspects of thecloud-based storage system 318 may be monitored (e.g., by a monitoringmodule that is executing in an EC2 instance) such that the cloud-basedstorage system 318 can be scaled-up or scaled-out as needed. Consider anexample in which the monitoring module monitors the performance of thecould-based storage system 318 via communications with one or more ofthe cloud computing instances 320, 322 that each are used to support theexecution of a storage controller application 324, 326, via monitoringcommunications between cloud computing instances 320, 322, 340 a, 340 b,340 n, via monitoring communications between cloud computing instances320, 322, 340 a, 340 b, 340 n and the cloud-based object storage 348, orin some other way. In such an example, assume that the monitoring moduledetermines that the cloud computing instances 320, 322 that are used tosupport the execution of a storage controller application 324, 326 areundersized and not sufficiently servicing the I/O requests that areissued by users of the cloud-based storage system 318. In such anexample, the monitoring module may create a new, more powerful cloudcomputing instance (e.g., a cloud computing instance of a type thatincludes more processing power, more memory, etc. . . . ) that includesthe storage controller application such that the new, more powerfulcloud computing instance can begin operating as the primary controller.Likewise, if the monitoring module determines that the cloud computinginstances 320, 322 that are used to support the execution of a storagecontroller application 324, 326 are oversized and that cost savingscould be gained by switching to a smaller, less powerful cloud computinginstance, the monitoring module may create a new, less powerful (andless expensive) cloud computing instance that includes the storagecontroller application such that the new, less powerful cloud computinginstance can begin operating as the primary controller.

Consider, as an additional example of dynamically sizing the cloud-basedstorage system 318, an example in which the monitoring module determinesthat the utilization of the local storage that is collectively providedby the cloud computing instances 340 a, 340 b, 340 n has reached apredetermined utilization threshold (e.g., 95%). In such an example, themonitoring module may create additional cloud computing instances withlocal storage to expand the pool of local storage that is offered by thecloud computing instances. Alternatively, the monitoring module maycreate one or more new cloud computing instances that have largeramounts of local storage than the already existing cloud computinginstances 340 a, 340 b, 340 n, such that data stored in an alreadyexisting cloud computing instance 340 a, 340 b, 340 n can be migrated tothe one or more new cloud computing instances and the already existingcloud computing instance 340 a, 340 b, 340 n can be terminated, therebyexpanding the pool of local storage that is offered by the cloudcomputing instances. Likewise, if the pool of local storage that isoffered by the cloud computing instances is unnecessarily large, datacan be consolidated and some cloud computing instances can beterminated.

Readers will appreciate that the cloud-based storage system 318 may besized up and down automatically by a monitoring module applying apredetermined set of rules that may be relatively simple of relativelycomplicated. In fact, the monitoring module may not only take intoaccount the current state of the cloud-based storage system 318, but themonitoring module may also apply predictive policies that are based on,for example, observed behavior (e.g., every night from 10 PM until 6 AMusage of the storage system is relatively light), predeterminedfingerprints (e.g., every time a virtual desktop infrastructure adds 100virtual desktops, the number of IOPS directed to the storage systemincrease by X), and so on. In such an example, the dynamic scaling ofthe cloud-based storage system 318 may be based on current performancemetrics, predicted workloads, and many other factors, includingcombinations thereof.

Readers will further appreciate that because the cloud-based storagesystem 318 may be dynamically scaled, the cloud-based storage system 318may even operate in a way that is more dynamic. Consider the example ofgarbage collection. In a traditional storage system, the amount ofstorage is fixed. As such, at some point the storage system may beforced to perform garbage collection as the amount of available storagehas become so constrained that the storage system is on the verge ofrunning out of storage. In contrast, the cloud-based storage system 318described here can always ‘add’ additional storage (e.g., by adding morecloud computing instances with local storage). Because the cloud-basedstorage system 318 described here can always ‘add’ additional storage,the cloud-based storage system 318 can make more intelligent decisionsregarding when to perform garbage collection. For example, thecloud-based storage system 318 may implement a policy that garbagecollection only be performed when the number of IOPS being serviced bythe cloud-based storage system 318 falls below a certain level. In someembodiments, other system-level functions (e.g., deduplication,compression) may also be turned off and on in response to system load,given that the size of the cloud-based storage system 318 is notconstrained in the same way that traditional storage systems areconstrained.

Readers will appreciate that embodiments of the present disclosureresolve an issue with block-storage services offered by some cloudcomputing environments as some cloud computing environments only allowfor one cloud computing instance to connect to a block-storage volume ata single time. For example, in Amazon AWS, only a single EC2 instancemay be connected to an EBS volume. Through the use of EC2 instances withlocal storage, embodiments of the present disclosure can offermulti-connect capabilities where multiple EC2 instances can connect toanother EC2 instance with local storage (‘a drive instance’). In suchembodiments, the drive instances may include software executing withinthe drive instance that allows the drive instance to support I/Odirected to a particular volume from each connected EC2 instance. Assuch, some embodiments of the present disclosure may be embodied asmulti-connect block storage services that may not include all of thecomponents depicted in FIG. 3C.

In some embodiments, especially in embodiments where the cloud-basedobject storage 348 resources are embodied as Amazon S3, the cloud-basedstorage system 318 may include one or more modules (e.g., a module ofcomputer program instructions executing on an EC2 instance) that areconfigured to ensure that when the local storage of a particular cloudcomputing instance is rehydrated with data from S3, the appropriate datais actually in S3. This issue arises largely because S3 implements aneventual consistency model where, when overwriting an existing object,reads of the object will eventually (but not necessarily immediately)become consistent and will eventually (but not necessarily immediately)return the overwritten version of the object. To address this issue, insome embodiments of the present disclosure, objects in S3 are neveroverwritten. Instead, a traditional ‘overwrite’ would result in thecreation of the new object (that includes the updated version of thedata) and the eventual deletion of the old object (that includes theprevious version of the data).

In some embodiments of the present disclosure, as part of an attempt tonever (or almost never) overwrite an object, when data is written to S3the resultant object may be tagged with a sequence number. In someembodiments, these sequence numbers may be persisted elsewhere (e.g., ina database) such that at any point in time, the sequence numberassociated with the most up-to-date version of some piece of data can beknown. In such a way, a determination can be made as to whether S3 hasthe most recent version of some piece of data by merely reading thesequence number associated with an object—and without actually readingthe data from S3. The ability to make this determination may beparticularly important when a cloud computing instance with localstorage crashes, as it would be undesirable to rehydrate the localstorage of a replacement cloud computing instance with out-of-date data.In fact, because the cloud-based storage system 318 does not need toaccess the data to verify its validity, the data can stay encrypted andaccess charges can be avoided.

The storage systems described above may carry out intelligent databackup techniques through which data stored in the storage system may becopied and stored in a distinct location to avoid data loss in the eventof equipment failure or some other form of catastrophe. For example, thestorage systems described above may be configured to examine each backupto avoid restoring the storage system to an undesirable state. Consideran example in which malware infects the storage system. In such anexample, the storage system may include software resources 314 that canscan each backup to identify backups that were captured before themalware infected the storage system and those backups that were capturedafter the malware infected the storage system. In such an example, thestorage system may restore itself from a backup that does not includethe malware—or at least not restore the portions of a backup thatcontained the malware. In such an example, the storage system mayinclude software resources 314 that can scan each backup to identify thepresences of malware (or a virus, or some other undesirable), forexample, by identifying write operations that were serviced by thestorage system and originated from a network subnet that is suspected tohave delivered the malware, by identifying write operations that wereserviced by the storage system and originated from a user that issuspected to have delivered the malware, by identifying write operationsthat were serviced by the storage system and examining the content ofthe write operation against fingerprints of the malware, and in manyother ways.

Readers will further appreciate that the backups (often in the form ofone or more snapshots) may also be utilized to perform rapid recovery ofthe storage system. Consider an example in which the storage system isinfected with ransomware that locks users out of the storage system. Insuch an example, software resources 314 within the storage system may beconfigured to detect the presence of ransomware and may be furtherconfigured to restore the storage system to a point-in-time, using theretained backups, prior to the point-in-time at which the ransomwareinfected the storage system. In such an example, the presence ofransomware may be explicitly detected through the use of software toolsutilized by the system, through the use of a key (e.g., a USB drive)that is inserted into the storage system, or in a similar way. Likewise,the presence of ransomware may be inferred in response to systemactivity meeting a predetermined fingerprint such as, for example, noreads or writes coming into the system for a predetermined period oftime.

Readers will appreciate that the various components described above maybe grouped into one or more optimized computing packages as convergedinfrastructures. Such converged infrastructures may include pools ofcomputers, storage and networking resources that can be shared bymultiple applications and managed in a collective manner usingpolicy-driven processes. Such converged infrastructures may beimplemented with a converged infrastructure reference architecture, withstandalone appliances, with a software driven hyper-converged approach(e.g., hyper-converged infrastructures), or in other ways.

Readers will appreciate that the storage systems described above may beuseful for supporting various types of software applications. Forexample, the storage system 306 may be useful in supporting artificialintelligence (‘AI’) applications, database applications, DevOpsprojects, electronic design automation tools, event-driven softwareapplications, high performance computing applications, simulationapplications, high-speed data capture and analysis applications, machinelearning applications, media production applications, media servingapplications, picture archiving and communication systems (‘PACS’)applications, software development applications, virtual realityapplications, augmented reality applications, and many other types ofapplications by providing storage resources to such applications.

The storage systems described above may operate to support a widevariety of applications. In view of the fact that the storage systemsinclude compute resources, storage resources, and a wide variety ofother resources, the storage systems may be well suited to supportapplications that are resource intensive such as, for example, AIapplications. AI applications may be deployed in a variety of fields,including: predictive maintenance in manufacturing and related fields,healthcare applications such as patient data & risk analytics, retailand marketing deployments (e.g., search advertising, social mediaadvertising), supply chains solutions, fintech solutions such asbusiness analytics & reporting tools, operational deployments such asreal-time analytics tools, application performance management tools, ITinfrastructure management tools, and many others.

Such AI applications may enable devices to perceive their environmentand take actions that maximize their chance of success at some goal.Examples of such AI applications can include IBM Watson, MicrosoftOxford, Google DeepMind, Baidu Minwa, and others. The storage systemsdescribed above may also be well suited to support other types ofapplications that are resource intensive such as, for example, machinelearning applications. Machine learning applications may perform varioustypes of data analysis to automate analytical model building. Usingalgorithms that iteratively learn from data, machine learningapplications can enable computers to learn without being explicitlyprogrammed. One particular area of machine learning is referred to asreinforcement learning, which involves taking suitable actions tomaximize reward in a particular situation. Reinforcement learning may beemployed to find the best possible behavior or path that a particularsoftware application or machine should take in a specific situation.Reinforcement learning differs from other areas of machine learning(e.g., supervised learning, unsupervised learning) in that correctinput/output pairs need not be presented for reinforcement learning andsub-optimal actions need not be explicitly corrected.

In addition to the resources already described, the storage systemsdescribed above may also include graphics processing units (‘GPUs’),occasionally referred to as visual processing unit (‘VPUs’). Such GPUsmay be embodied as specialized electronic circuits that rapidlymanipulate and alter memory to accelerate the creation of images in aframe buffer intended for output to a display device. Such GPUs may beincluded within any of the computing devices that are part of thestorage systems described above, including as one of many individuallyscalable components of a storage system, where other examples ofindividually scalable components of such storage system can includestorage components, memory components, compute components (e.g., CPUs,FPGAs, ASICs), networking components, software components, and others.In addition to GPUs, the storage systems described above may alsoinclude neural network processors (‘NNPs’) for use in various aspects ofneural network processing. Such NNPs may be used in place of (or inaddition to) GPUs and may also be independently scalable.

As described above, the storage systems described herein may beconfigured to support artificial intelligence applications, machinelearning applications, big data analytics applications, and many othertypes of applications. The rapid growth in these sort of applications isbeing driven by three technologies: deep learning (DL), GPU processors,and Big Data. Deep learning is a computing model that makes use ofmassively parallel neural networks inspired by the human brain. Insteadof experts handcrafting software, a deep learning model writes its ownsoftware by learning from lots of examples. Such GPUs may includethousands of cores that are well-suited to run algorithms that looselyrepresent the parallel nature of the human brain.

Advances in deep neural networks have ignited a new wave of algorithmsand tools for data scientists to tap into their data with artificialintelligence (AI). With improved algorithms, larger data sets, andvarious frameworks (including open-source software libraries for machinelearning across a range of tasks), data scientists are tackling new usecases like autonomous driving vehicles, natural language processing andunderstanding, computer vision, machine reasoning, strong AI, and manyothers. Applications of such techniques may include: machine andvehicular object detection, identification and avoidance; visualrecognition, classification and tagging; algorithmic financial tradingstrategy performance management; simultaneous localization and mapping;predictive maintenance of high-value machinery; prevention against cybersecurity threats, expertise automation; image recognition andclassification; question answering; robotics; text analytics(extraction, classification) and text generation and translation; andmany others. Applications of AI techniques has materialized in a widearray of products include, for example, Amazon Echo's speech recognitiontechnology that allows users to talk to their machines, GoogleTranslate™ which allows for machine-based language translation,Spotify's Discover Weekly that provides recommendations on new songs andartists that a user may like based on the user's usage and trafficanalysis, Quill's text generation offering that takes structured dataand turns it into narrative stories, Chatbots that provide real-time,contextually specific answers to questions in a dialog format, and manyothers.

Data is the heart of modern AI and deep learning algorithms. Beforetraining can begin, one problem that must be addressed revolves aroundcollecting the labeled data that is crucial for training an accurate AImodel. A full scale AI deployment may be required to continuouslycollect, clean, transform, label, and store large amounts of data.Adding additional high quality data points directly translates to moreaccurate models and better insights. Data samples may undergo a seriesof processing steps including, but not limited to: 1) ingesting the datafrom an external source into the training system and storing the data inraw form, 2) cleaning and transforming the data in a format convenientfor training, including linking data samples to the appropriate label,3) exploring parameters and models, quickly testing with a smallerdataset, and iterating to converge on the most promising models to pushinto the production cluster, 4) executing training phases to selectrandom batches of input data, including both new and older samples, andfeeding those into production GPU servers for computation to updatemodel parameters, and 5) evaluating including using a holdback portionof the data not used in training in order to evaluate model accuracy onthe holdout data. This lifecycle may apply for any type of parallelizedmachine learning, not just neural networks or deep learning. Forexample, standard machine learning frameworks may rely on CPUs insteadof GPUs but the data ingest and training workflows may be the same.Readers will appreciate that a single shared storage data hub creates acoordination point throughout the lifecycle without the need for extradata copies among the ingest, preprocessing, and training stages. Rarelyis the ingested data used for only one purpose, and shared storage givesthe flexibility to train multiple different models or apply traditionalanalytics to the data.

Readers will appreciate that each stage in the AI data pipeline may havevarying requirements from the data hub (e.g., the storage system orcollection of storage systems). Scale-out storage systems must deliveruncompromising performance for all manner of access types andpatterns—from small, metadata-heavy to large files, from random tosequential access patterns, and from low to high concurrency. Thestorage systems described above may serve as an ideal AI data hub as thesystems may service unstructured workloads. In the first stage, data isideally ingested and stored on to the same data hub that followingstages will use, in order to avoid excess data copying. The next twosteps can be done on a standard compute server that optionally includesa GPU, and then in the fourth and last stage, full training productionjobs are run on powerful GPU-accelerated servers. Often, there is aproduction pipeline alongside an experimental pipeline operating on thesame dataset. Further, the GPU-accelerated servers can be usedindependently for different models or joined together to train on onelarger model, even spanning multiple systems for distributed training.If the shared storage tier is slow, then data must be copied to localstorage for each phase, resulting in wasted time staging data ontodifferent servers. The ideal data hub for the AI training pipelinedelivers performance similar to data stored locally on the server nodewhile also having the simplicity and performance to enable all pipelinestages to operate concurrently.

Although the preceding paragraphs discuss deep learning applications,readers will appreciate that the storage systems described herein mayalso be part of a distributed deep learning (‘DDL’) platform to supportthe execution of DDL algorithms. The storage systems described above mayalso be paired with other technologies such as TensorFlow, anopen-source software library for dataflow programming across a range oftasks that may be used for machine learning applications such as neuralnetworks, to facilitate the development of such machine learning models,applications, and so on.

The storage systems described above may also be used in a neuromorphiccomputing environment. Neuromorphic computing is a form of computingthat mimics brain cells. To support neuromorphic computing, anarchitecture of interconnected “neurons” replace traditional computingmodels with low-powered signals that go directly between neurons formore efficient computation. Neuromorphic computing may make use ofvery-large-scale integration (VLSI) systems containing electronic analogcircuits to mimic neuro-biological architectures present in the nervoussystem, as well as analog, digital, mixed-mode analog/digital VLSI, andsoftware systems that implement models of neural systems for perception,motor control, or multisensory integration.

Readers will appreciate that the storage systems described above may beconfigured to support the storage or use of (among other types of data)blockchains. In addition to supporting the storage and use of blockchaintechnologies, the storage systems described above may also support thestorage and use of derivative items such as, for example, open sourceblockchains and related tools that are part of the IBM™ Hyperledgerproject, permissioned blockchains in which a certain number of trustedparties are allowed to access the block chain, blockchain products thatenable developers to build their own distributed ledger projects, andothers. Blockchains and the storage systems described herein may beleveraged to support on-chain storage of data as well as off-chainstorage of data.

Off-chain storage of data can be implemented in a variety of ways andcan occur when the data itself is not stored within the blockchain. Forexample, in one embodiment, a hash function may be utilized and the dataitself may be fed into the hash function to generate a hash value. Insuch an example, the hashes of large pieces of data may be embeddedwithin transactions, instead of the data itself. Readers will appreciatethat, in other embodiments, alternatives to blockchains may be used tofacilitate the decentralized storage of information. For example, onealternative to a blockchain that may be used is a blockweave. Whileconventional blockchains store every transaction to achieve validation,a blockweave permits secure decentralization without the usage of theentire chain, thereby enabling low cost on-chain storage of data. Suchblockweaves may utilize a consensus mechanism that is based on proof ofaccess (PoA) and proof of work (PoW).

The storage systems described above may, either alone or in combinationwith other computing devices, be used to support in-memory computingapplications. In-memory computing involves the storage of information inRAM that is distributed across a cluster of computers. Readers willappreciate that the storage systems described above, especially thosethat are configurable with customizable amounts of processing resources,storage resources, and memory resources (e.g., those systems in whichblades that contain configurable amounts of each type of resource), maybe configured in a way so as to provide an infrastructure that cansupport in-memory computing. Likewise, the storage systems describedabove may include component parts (e.g., NVDIMMs, 3D crosspoint storagethat provide fast random access memory that is persistent) that canactually provide for an improved in-memory computing environment ascompared to in-memory computing environments that rely on RAMdistributed across dedicated servers.

In some embodiments, the storage systems described above may beconfigured to operate as a hybrid in-memory computing environment thatincludes a universal interface to all storage media (e.g., RAM, flashstorage, 3D crosspoint storage). In such embodiments, users may have noknowledge regarding the details of where their data is stored but theycan still use the same full, unified API to address data. In suchembodiments, the storage system may (in the background) move data to thefastest layer available—including intelligently placing the data independence upon various characteristics of the data or in dependenceupon some other heuristic. In such an example, the storage systems mayeven make use of existing products such as Apache Ignite and GridGain tomove data between the various storage layers, or the storage systems maymake use of custom software to move data between the various storagelayers. The storage systems described herein may implement variousoptimizations to improve the performance of in-memory computing such as,for example, having computations occur as close to the data as possible.

Readers will further appreciate that in some embodiments, the storagesystems described above may be paired with other resources to supportthe applications described above. For example, one infrastructure couldinclude primary compute in the form of servers and workstations whichspecialize in using General-purpose computing on graphics processingunits (‘GPGPU’) to accelerate deep learning applications that areinterconnected into a computation engine to train parameters for deepneural networks. Each system may have Ethernet external connectivity,InfiniBand external connectivity, some other form of externalconnectivity, or some combination thereof. In such an example, the GPUscan be grouped for a single large training or used independently totrain multiple models. The infrastructure could also include a storagesystem such as those described above to provide, for example, ascale-out all-flash file or object store through which data can beaccessed via high-performance protocols such as NFS, S3, and so on. Theinfrastructure can also include, for example, redundant top-of-rackEthernet switches connected to storage and compute via ports in MLAGport channels for redundancy. The infrastructure could also includeadditional compute in the form of whitebox servers, optionally withGPUs, for data ingestion, pre-processing, and model debugging. Readerswill appreciate that additional infrastructures are also be possible.

Readers will appreciate that the storage systems described above, eitheralone or in coordination with other computing machinery may beconfigured to support other AI related tools. For example, the storagesystems may make use of tools like ONXX or other open neural networkexchange formats that make it easier to transfer models written indifferent AI frameworks. Likewise, the storage systems may be configuredto support tools like Amazon's Gluon that allow developers to prototype,build, and train deep learning models. In fact, the storage systemsdescribed above may be part of a larger platform, such as IBM™ CloudPrivate for Data, that includes integrated data science, dataengineering and application building services.

Readers will further appreciate that the storage systems described abovemay also be deployed as an edge solution. Such an edge solution may bein place to optimize cloud computing systems by performing dataprocessing at the edge of the network, near the source of the data. Edgecomputing can push applications, data and computing power (i.e.,services) away from centralized points to the logical extremes of anetwork. Through the use of edge solutions such as the storage systemsdescribed above, computational tasks may be performed using the computeresources provided by such storage systems, data may be storage usingthe storage resources of the storage system, and cloud-based servicesmay be accessed through the use of various resources of the storagesystem (including networking resources). By performing computationaltasks on the edge solution, storing data on the edge solution, andgenerally making use of the edge solution, the consumption of expensivecloud-based resources may be avoided and, in fact, performanceimprovements may be experienced relative to a heavier reliance oncloud-based resources.

While many tasks may benefit from the utilization of an edge solution,some particular uses may be especially suited for deployment in such anenvironment. For example, devices like drones, autonomous cars, robots,and others may require extremely rapid processing—so fast, in fact, thatsending data up to a cloud environment and back to receive dataprocessing support may simply be too slow. As an additional example,some IoT devices such as connected video cameras may not be well-suitedfor the utilization of cloud-based resources as it may be impractical(not only from a privacy perspective, security perspective, or afinancial perspective) to send the data to the cloud simply because ofthe pure volume of data that is involved. As such, many tasks thatreally on data processing, storage, or communications may be bettersuited by platforms that include edge solutions such as the storagesystems described above.

The storage systems described above may alone, or in combination withother computing resources, serves as a network edge platform thatcombines compute resources, storage resources, networking resources,cloud technologies and network virtualization technologies, and so on.As part of the network, the edge may take on characteristics similar toother network facilities, from the customer premise and backhaulaggregation facilities to Points of Presence (PoPs) and regional datacenters. Readers will appreciate that network workloads, such as VirtualNetwork Functions (VNFs) and others, will reside on the network edgeplatform. Enabled by a combination of containers and virtual machines,the network edge platform may rely on controllers and schedulers thatare no longer geographically co-located with the data processingresources. The functions, as microservices, may split into controlplanes, user and data planes, or even state machines, allowing forindependent optimization and scaling techniques to be applied. Such userand data planes may be enabled through increased accelerators, boththose residing in server platforms, such as FPGAs and Smart NICs, andthrough SDN-enabled merchant silicon and programmable ASICs.

The storage systems described above may also be optimized for use in bigdata analytics. Big data analytics may be generally described as theprocess of examining large and varied data sets to uncover hiddenpatterns, unknown correlations, market trends, customer preferences andother useful information that can help organizations make more-informedbusiness decisions. As part of that process, semi-structured andunstructured data such as, for example, internet clickstream data, webserver logs, social media content, text from customer emails and surveyresponses, mobile-phone call-detail records, IoT sensor data, and otherdata may be converted to a structured form.

The storage systems described above may also support (includingimplementing as a system interface) applications that perform tasks inresponse to human speech. For example, the storage systems may supportthe execution intelligent personal assistant applications such as, forexample, Amazon's Alexa, Apple Siri, Google Voice, Samsung Bixby,Microsoft Cortana, and others. While the examples described in theprevious sentence make use of voice as input, the storage systemsdescribed above may also support chatbots, talkbots, chatterbots, orartificial conversational entities or other applications that areconfigured to conduct a conversation via auditory or textual methods.Likewise, the storage system may actually execute such an application toenable a user such as a system administrator to interact with thestorage system via speech. Such applications are generally capable ofvoice interaction, music playback, making to-do lists, setting alarms,streaming podcasts, playing audiobooks, and providing weather, traffic,and other real time information, such as news, although in embodimentsin accordance with the present disclosure, such applications may beutilized as interfaces to various system management operations.

The storage systems described above may also implement AI platforms fordelivering on the vision of self-driving storage. Such AI platforms maybe configured to deliver global predictive intelligence by collectingand analyzing large amounts of storage system telemetry data points toenable effortless management, analytics and support. In fact, suchstorage systems may be capable of predicting both capacity andperformance, as well as generating intelligent advice on workloaddeployment, interaction and optimization. Such AI platforms may beconfigured to scan all incoming storage system telemetry data against alibrary of issue fingerprints to predict and resolve incidents inreal-time, before they impact customer environments, and captureshundreds of variables related to performance that are used to forecastperformance load.

The storage systems described above may support the serialized orsimultaneous execution of artificial intelligence applications, machinelearning applications, data analytics applications, datatransformations, and other tasks that collectively may form an AIladder. Such an AI ladder may effectively be formed by combining suchelements to form a complete data science pipeline, where existdependencies between elements of the AI ladder. For example, AI mayrequire that some form of machine learning has taken place, machinelearning may require that some form of analytics has taken place,analytics may require that some form of data and informationarchitecting has taken place, and so on. As such, each element may beviewed as a rung in an AI ladder that collectively can form a completeand sophisticated AI solution.

The storage systems described above may also, either alone or incombination with other computing environments, be used to deliver an AIeverywhere experience where AI permeates wide and expansive aspects ofbusiness and life. For example, AI may play an important role in thedelivery of deep learning solutions, deep reinforcement learningsolutions, artificial general intelligence solutions, autonomousvehicles, cognitive computing solutions, commercial UAVs or drones,conversational user interfaces, enterprise taxonomies, ontologymanagement solutions, machine learning solutions, smart dust, smartrobots, smart workplaces, and many others.

The storage systems described above may also, either alone or incombination with other computing environments, be used to deliver a widerange of transparently immersive experiences (including those that usedigital twins of various “things” such as people, places, processes,systems, and so on) where technology can introduce transparency betweenpeople, businesses, and things. Such transparently immersive experiencesmay be delivered as augmented reality technologies, connected homes,virtual reality technologies, brain-computer interfaces, humanaugmentation technologies, nanotube electronics, volumetric displays, 4Dprinting technologies, or others.

The storage systems described above may also, either alone or incombination with other computing environments, be used to support a widevariety of digital platforms. Such digital platforms can include, forexample, 5G wireless systems and platforms, digital twin platforms, edgecomputing platforms, IoT platforms, quantum computing platforms,serverless PaaS, software-defined security, neuromorphic computingplatforms, and so on.

The storage systems described above may also be part of a multi-cloudenvironment in which multiple cloud computing and storage services aredeployed in a single heterogeneous architecture. In order to facilitatethe operation of such a multi-cloud environment, DevOps tools may bedeployed to enable orchestration across clouds. Likewise, continuousdevelopment and continuous integration tools may be deployed tostandardize processes around continuous integration and delivery, newfeature rollout and provisioning cloud workloads. By standardizing theseprocesses, a multi-cloud strategy may be implemented that enables theutilization of the best provider for each workload.

The storage systems described above may be used as a part of a platformto enable the use of crypto-anchors that may be used to authenticate aproduct's origins and contents to ensure that it matches a blockchainrecord associated with the product. Similarly, as part of a suite oftools to secure data stored on the storage system, the storage systemsdescribed above may implement various encryption technologies andschemes, including lattice cryptography. Lattice cryptography caninvolve constructions of cryptographic primitives that involve lattices,either in the construction itself or in the security proof. Unlikepublic-key schemes such as the RSA, Diffie-Hellman or Elliptic-Curvecryptosystems, which are easily attacked by a quantum computer, somelattice-based constructions appear to be resistant to attack by bothclassical and quantum computers.

A quantum computer is a device that performs quantum computing. Quantumcomputing is computing using quantum-mechanical phenomena, such assuperposition and entanglement. Quantum computers differ fromtraditional computers that are based on transistors, as such traditionalcomputers require that data be encoded into binary digits (bits), eachof which is always in one of two definite states (0 or 1). In contrastto traditional computers, quantum computers use quantum bits, which canbe in superpositions of states. A quantum computer maintains a sequenceof qubits, where a single qubit can represent a one, a zero, or anyquantum superposition of those two qubit states. A pair of qubits can bein any quantum superposition of 4 states, and three qubits in anysuperposition of 8 states. A quantum computer with n qubits cangenerally be in an arbitrary superposition of up to 2{circumflex over( )}n different states simultaneously, whereas a traditional computercan only be in one of these states at any one time. A quantum Turingmachine is a theoretical model of such a computer.

The storage systems described above may also be paired withFPGA-accelerated servers as part of a larger AI or ML infrastructure.Such FPGA-accelerated servers may reside near (e.g., in the same datacenter) the storage systems described above or even incorporated into anappliance that includes one or more storage systems, one or moreFPGA-accelerated servers, networking infrastructure that supportscommunications between the one or more storage systems and the one ormore FPGA-accelerated servers, as well as other hardware and softwarecomponents. Alternatively, FPGA-accelerated servers may reside within acloud computing environment that may be used to perform compute-relatedtasks for AI and ML jobs. Any of the embodiments described above may beused to collectively serve as a FPGA-based AI or ML platform. Readerswill appreciate that, in some embodiments of the FPGA-based AI or MLplatform, the FPGAs that are contained within the FPGA-acceleratedservers may be reconfigured for different types of ML models (e.g.,LSTMs, CNNs, GRUs). The ability to reconfigure the FPGAs that arecontained within the FPGA-accelerated servers may enable theacceleration of a ML or AI application based on the most optimalnumerical precision and memory model being used. Readers will appreciatethat by treating the collection of FPGA-accelerated servers as a pool ofFPGAs, any CPU in the data center may utilize the pool of FPGAs as ashared hardware microservice, rather than limiting a server to dedicatedaccelerators plugged into it.

The FPGA-accelerated servers and the GPU-accelerated servers describedabove may implement a model of computing where, rather than keeping asmall amount of data in a CPU and running a long stream of instructionsover it as occurred in more traditional computing models, the machinelearning model and parameters are pinned into the high-bandwidth on-chipmemory with lots of data streaming though the high-bandwidth on-chipmemory. FPGAs may even be more efficient than GPUs for this computingmodel, as the FPGAs can be programmed with only the instructions neededto run this kind of computing model.

The storage systems described above may be configured to provideparallel storage, for example, through the use of a parallel file systemsuch as BeeGFS. Such parallel files systems may include a distributedmetadata architecture. For example, the parallel file system may includea plurality of metadata servers across which metadata is distributed, aswell as components that include services for clients and storageservers.

The systems described above can support the execution of a wide array ofsoftware applications. Such software applications can be deployed in avariety of ways, including container-based deployment models.Containerized applications may be managed using a variety of tools. Forexample, containerized applications may be managed using Docker Swarm,Kubernetes, and others. Containerized applications may be used tofacilitate a serverless, cloud native computing deployment andmanagement model for software applications. In support of a serverless,cloud native computing deployment and management model for softwareapplications, containers may be used as part of an event handlingmechanisms (e.g., AWS Lambdas) such that various events cause acontainerized application to be spun up to operate as an event handler.

The systems described above may be deployed in a variety of ways,including being deployed in ways that support fifth generation (‘5G’)networks. 5G networks may support substantially faster datacommunications than previous generations of mobile communicationsnetworks and, as a consequence may lead to the disaggregation of dataand computing resources as modern massive data centers may become lessprominent and may be replaced, for example, by more-local, micro datacenters that are close to the mobile-network towers. The systemsdescribed above may be included in such local, micro data centers andmay be part of or paired to multi-access edge computing (‘MEC’) systems.Such MEC systems may enable cloud computing capabilities and an ITservice environment at the edge of the cellular network. By runningapplications and performing related processing tasks closer to thecellular customer, network congestion may be reduced and applicationsmay perform better.

For further explanation, FIG. 3D illustrates an exemplary computingdevice 350 that may be specifically configured to perform one or more ofthe processes described herein. As shown in FIG. 3D, computing device350 may include a communication interface 352, a processor 354, astorage device 356, and an input/output (“I/O”) module 358communicatively connected one to another via a communicationinfrastructure 360. While an exemplary computing device 350 is shown inFIG. 3D, the components illustrated in FIG. 3D are not intended to belimiting. Additional or alternative components may be used in otherembodiments. Components of computing device 350 shown in FIG. 3D willnow be described in additional detail.

Communication interface 352 may be configured to communicate with one ormore computing devices. Examples of communication interface 352 include,without limitation, a wired network interface (such as a networkinterface card), a wireless network interface (such as a wirelessnetwork interface card), a modem, an audio/video connection, and anyother suitable interface.

Processor 354 generally represents any type or form of processing unitcapable of processing data and/or interpreting, executing, and/ordirecting execution of one or more of the instructions, processes,and/or operations described herein. Processor 354 may perform operationsby executing computer-executable instructions 362 (e.g., an application,software, code, and/or other executable data instance) stored in storagedevice 356.

Storage device 356 may include one or more data storage media, devices,or configurations and may employ any type, form, and combination of datastorage media and/or device. For example, storage device 356 mayinclude, but is not limited to, any combination of the non-volatilemedia and/or volatile media described herein. Electronic data, includingdata described herein, may be temporarily and/or permanently stored instorage device 356. For example, data representative ofcomputer-executable instructions 362 configured to direct processor 354to perform any of the operations described herein may be stored withinstorage device 356. In some examples, data may be arranged in one ormore databases residing within storage device 356.

I/O module 358 may include one or more I/O modules configured to receiveuser input and provide user output. I/O module 358 may include anyhardware, firmware, software, or combination thereof supportive of inputand output capabilities. For example, I/O module 358 may includehardware and/or software for capturing user input, including, but notlimited to, a keyboard or keypad, a touchscreen component (e.g.,touchscreen display), a receiver (e.g., an RF or infrared receiver),motion sensors, and/or one or more input buttons.

I/O module 358 may include one or more devices for presenting output toa user, including, but not limited to, a graphics engine, a display(e.g., a display screen), one or more output drivers (e.g., displaydrivers), one or more audio speakers, and one or more audio drivers. Incertain embodiments, I/O module 358 is configured to provide graphicaldata to a display for presentation to a user. The graphical data may berepresentative of one or more graphical user interfaces and/or any othergraphical content as may serve a particular implementation. In someexamples, any of the systems, computing devices, and/or other componentsdescribed herein may be implemented by computing device 350.

For further explanation, FIG. 4 sets forth a flow chart illustrating anexample method of providing data management as-a-service in accordancewith some embodiments of the present disclosure. Although not depictedin FIG. 4, the method illustrated in FIG. 4 may be carried out, at leastin part, by one or more data services modules. The one or more dataservices modules may be embodied, for example, as computer programinstructions executing on virtualized computer hardware such as avirtual machine, as computer program instructions executing with acontainer, or embodied in some other way. In such an example, the one ormore data services modules may be executing in a public cloudenvironment such as Amazon AWS™, Microsoft Azure™, and so on.Alternatively, the one or more data services modules may be executing ina private cloud environment, in a hybrid cloud environment, on dedicatedhardware and software as would be found in a datacenter, or in someother environment.

The one or more data services modules may be configured to present oneor more available data services to a user, receive a selection of one ormore selected data services, and at least assist in the process ofapplying, in dependence upon the one or more selected data services, oneor more data services policies to a dataset associated with the user, aswill be described in greater detail below. Furthermore, the one or moredata services modules may be configured to perform other steps as willbe described in greater detail below. In such a way, the one or moredata services modules may essentially act as a gateway to physicaldevices such as one or more storage systems (including, for example, thestorage systems described above as well as their variants), one or morenetworking devices, one or more processing devices, and other devicesthat can drive the operation of such devices so as to deliver a widearray of data services.

The example method depicted in FIG. 4 includes presenting 402 one ormore available data services to a user. The one or more available dataservices may be embodied as services that may be provided to consumers(i.e., a user) of the data services to manage data that is associatedwith the consumer of the data services. Data services may be applied tovarious forms of data including, for example, to one or more files in afile system, to one or more objects, to one or more blocks of data thatcollectively form a dataset, to one or more blocks of data thatcollectively form a volume, to multiple volumes, and so on. Dataservices may be applied to a user selected dataset or, alternatively,may be applied to some data that is selected based on one or morepolicies or heuristics. For example, some data (e.g., data that haspersonally identifiable information) in a dataset may have one set ofdata services applied to it whereas other data (e.g., data that does nothave personally identifiable information) in the same dataset may have adifferent set of data services applied to it.

As an illustrative example of available data services that may bepresented 402 to a user, data services may be presented 402 to the userthat are associated with different levels of data protection. Forexample, data services may be presented to the user that, when selectedand enforced, guarantee the user that data associated with that userwill be protected such that various recovery point objectives (‘RPO’)can be guaranteed. A first available data service may ensure, forexample, that some dataset associated with the user will be protectedsuch that any data that is more than 5 seconds old can be recovered inthe event of a failure of the primary data store whereas a secondavailable data service may ensure that the dataset that is associatedwith the user will be protected such that any data that is more than 5minutes old can be recovered in the event of a failure of the primarydata store. Readers will appreciate that this is just one example ofavailable date services that may be presented 402 to the user.Additional available date services that may be presented 402 to the userwill be described in greater detail below.

The example method depicted in FIG. 4 also includes receiving 404 aselection of one or more selected data services. The one or moreselected data services may represent data services, chosen from the setof all available data services, that the user would like to have appliedto a particular dataset that is associated with the user. Continuingwith the example described above in which a first available data servicemay ensure that some dataset associated with the user will be protectedsuch that any data that is more than 5 seconds old can be recovered inthe event of a failure of the primary data store whereas a secondavailable data service may ensure that the dataset that is associatedwith the user will be protected such that any data that is more than 5minutes old can be recovered in the event of a failure of the primarydata store, the user may select the first available data service tominimize data loss in the event of a failure of the primary data store.Readers will appreciate, however, that the monetary cost of the firstavailable data service may be higher than the monetary cost of thesecond available data service, such that user's can weigh these costdifferences and chose a data service that is most appropriate for theirapplications, data, and so on. The user's selection of one or moreselected data services may be received 404, for example, via a GUI thatis presented to the user, as part of a performance tier selected by theuser, or in some other way.

The example method depicted in FIG. 4 also includes applying 406, independence upon the one or more selected data services, one or more dataservices policies to a dataset associated with the user. The one or moredata services policies may be embodied, for example, as one or morerules that, when enforced or implemented, causes an associated dataservice to be provided. Continuing with the example described above inwhich a user selected a first available data service to ensure that somedataset associated with the user will be protected such that any datathat is more than 5 seconds old can be recovered in the event of afailure of the primary data store, the one or more data servicespolicies that may be applied 406 can include, for example, a policy thatcauses a snapshot of the user's dataset to be taken every 5 seconds andsent to one or more backup storage systems. In such a way, even if theprimary data store failed or otherwise became unavailable, a snapshotwould have been taken no more than 5 seconds prior to the failure, andsuch a snapshot could be recovered from one or more backup storagesystems. In other examples, other data services policies may be applied406 to replay I/O events that caused the dataset to be modified so as toprovide the associated selected data services, or some other dataservices policies may be applied 406 that cause the associated selecteddata services to be provided.

Readers will appreciate that in some embodiments, where one or more dataservices modules may be configured to present 402 one or more availabledata services to a user and to receive 404 a selection of one or moreselected data services, the one or more data services modules may not beresponsible for applying 406 the one or more data services policies to adataset associated with the user. Instead, the one or more data servicesmodules may instruct (via one or more APIs, via one or more messages, orin some other way) some other entity to apply 406 the one or more dataservices policies to the dataset that is associated with the user. Forexample, the one or more data services modules may utilize APIs providedby storage system to cause the storage system to carry out backupoperations as described above in order to provide the first availabledata service described above in which a dataset associated with the userwill be protected such that any data that is more than 5 seconds old canbe recovered in the event of a failure of the primary data store.

Readers will appreciate that many data services may be presented 402 toa user, selected by a user, and ultimately result in one or more dataservices policies being applied 406 to a dataset associated with theuser in dependence upon the one or more data services selected by theuser. A non-exhaustive list of data services that may be made availableis included below, although readers will appreciate that additional dataservices may be made available in accordance with some embodiments ofthe present disclosure.

One example of data services that may be presented 402 to a user,selected by a user (where such a selection is received 404 by one ormore data services modules or similar mechanism), and ultimately applied406 to a dataset associated with the user can include one or more datacompliance services. Such data compliance services may be embodied, forexample, as services that may be provided to consumers (i.e., a user)the data compliance services to ensure that the user's datasets aremanaged in a way to adhere to various regulatory requirements. Forexample, one or more data compliance services may be offered to a userto ensure that the user's datasets are managed in a way so as to adhereto the General Data Protection Regulation (‘GDPR’), one or datacompliance services may be offered to a user to ensure that the user'sdatasets are managed in a way so as to adhere to the Sarbanes-Oxley Actof 2002 (‘SOX’), or one or more data compliance services may be offeredto a user to ensure that the user's datasets are managed in a way so asto adhere to some other regulatory act. In addition, the one or moredata compliance services may be offered to a user to ensure that theuser's datasets are managed in a way so as to adhere to somenon-governmental guidance (e.g., to adhere to best practices forauditing purposes), the one or more data compliance services may beoffered to a user to ensure that the user's datasets are managed in away so as to adhere to a particular clients or organizationsrequirements, and so on. In this example, the data compliance servicesmay be presented 402 to a user, a selection of one or more selected dataservices may be received 404, and the selected data compliance servicesmay be applied 406 to a dataset that is associated with the user.

Consider an example in which a particular data compliance service isdesigned to ensure that a user's datasets are managed in a way so as toadhere to the requirements set forth in the GDPR. While a listing of allrequirements of the GDPR can be found in the regulation itself, for thepurposes of illustration, an example requirement set forth in the GDPRrequires that pseudonymization processes must be applied to stored datain order to transform personal data in such a way that the resultingdata cannot be attributed to a specific data subject without the use ofadditional information. For example, data encryption techniques can beapplied to render the original data unintelligible, and such dataencryption techniques cannot be reversed without access to the correctdecryption key. As such, the GDPR may require that the decryption key bekept separately from the pseudonymised data. One particular datacompliance service may be offered to ensure adherence to therequirements set forth in this paragraph.

In order to provide this particular data compliance service, the datacompliance service may be presented 402 to a user (e.g., via a GUI) andselected by the user, thereby causing a selection of the particular datacompliance service to be received 404. In response to receiving 404 theselection of the particular data compliance service, one or more dataservices policies may be applied 406 to a dataset associated with theuser to carry out the particular data compliance service. For example, adata services policy may be applied requiring that the dataset beencrypted prior to be stored in a storage system, prior to being storedin a cloud environment, or prior to being stored elsewhere. In order toenforce this policy, a requirement may be enforced not only requiringthat the dataset be encrypted when stored, but a requirement may be putin place requiring that the dataset be encrypted prior to transmittingthe dataset (e.g., sending the dataset to another party). In such anexample, a data services policy may also be put in place requiring thatany encryption keys used to encrypt the dataset are not stored on thesame system that stores the dataset itself. Readers will appreciate thatmany other forms of data compliance services may be offered andimplemented in accordance with embodiments of the present disclosure.

Another example of data services that may be presented 402 to a user,selected by a user (where such a selection is received 404 by one ormore data services modules or similar mechanism), and ultimately applied406 to a dataset associated with the user can include one or more highavailability data services. Such high availability data services may beembodied, for example, as services that may be provided to consumers(i.e., a user) of the high availability data services to ensure that theuser's datasets are guaranteed to have a particular level of uptime(i.e., to be available a predetermined amount of time). For example, afirst high availability data service may be offered to a user to ensurethat the user's dataset has three nine of availability, meaning that thedataset is available 99.9% of the time. A second high availability dataservice may be offered, however, to a user to ensure that the user'sdataset has five nines of availability, meaning that the dataset isavailable 99.999% of the time. Other high availability data services maybe offered that ensure other levels of availability. Likewise, the highavailability data services may also be delivered in such a way so as toensure various levels of uptime for entities other than the dataset. Forexample, a particular high availability data service may ensure that oneor more virtual machines, one or more containers, one or more dataaccess endpoints, or some other entity is available a particular amountof time. In this example, the high availability data services may bepresented 402 to a user, a selection of one or more selected dataservices may be received 404, and the selected high availability dataservices may be applied 406 to a dataset (or other entity) that isassociated with the user.

Consider an example in which a particular high availability data serviceis designed to ensure that the user's dataset has five nines ofavailability, meaning that the dataset is available 99.999% of the time.In such an example and in order to deliver on this uptime guarantee, oneor more data services policies that are associated with such a highavailability data service may be applied and enforced. For example, adata services policy may be applied requiring that the dataset bemirrored across a predetermined number of storage systems, a dataservices policy may be applied requiring that the dataset be mirroredacross a predetermined number of availability zones in a cloudenvironment, a data services policy may be applied requiring that thedataset be replicated in a particular way (e.g., synchronouslyreplicated such that multiple up-to-date copies of the dataset exist),and so on.

Readers will appreciate that many other forms of high availability dataservices may be offered and implemented in accordance with embodimentsof the present disclosure. For example, such high availability dataservices may be associated with data services policies that not onlyguarantee that a particular number of copies of the dataset exist inorder to protect against failures of the storage infrastructure (e.g., afailure of the storage system itself), but high availability dataservices may also be associated with data service policies that aredesigned to prevent the dataset from becoming unavailable for otherreasons. For example, a high availability data service may be associatedwith one or more data services policies that require a particular levelof redundancy in networking paths so that the availability to meet theuptime requirements associated with the dataset are not comprised by alack of data communications paths to access the storage resources thatcontain the dataset. In other embodiments of the present disclosure,additional forms of high availability data services may be offered andimplemented.

Another example of data services that may be presented 402 to a user,selected by a user (where such a selection is received 404 by one ormore data services modules or similar mechanism), and ultimately applied406 to a dataset associated with the user can include one or moredisaster recovery services. Such disaster recovery services may beembodied, for example, as services that may be provided to consumers(i.e., a user) of the disaster recovery services to ensure that a user'sdataset may be recovered in accordance with certain parameters in theevent of a disaster. For example, a first disaster recovery service maybe offered to a user to ensure that the user's dataset can be recoveredin accordance with a first RPO and a first recovery time objective(‘RTO’) in the event that the storage system that stores the datasetfails, or some other form of disaster occurs that causes the dataset tobecome unavailable. A second disaster recovery service may be offered,however, to a user to ensure that the user's dataset can be recovered inaccordance with a second RPO and a second RTO in the event that thestorage system that stores the dataset fails, or some other form ofdisaster occurs that causes the dataset to become unavailable. Otherdisaster recovery services may be offered that ensure other levels ofrecoverability outside of the scope of RPO and RTO. Likewise, thedisaster recovery services may also be delivered in such a way so as toensure various levels of recoverability for entities other than thedataset. For example, a particular disaster recovery service may ensurethat one or more virtual machines, one or more containers, one or moredata access endpoints, or some other entity can be recovered in acertain amount of time or in accordance with some other metric. In thisexample, the disaster recovery services may be presented 402 to a user,a selection of one or more selected disaster recovery services may bereceived 404, and the selected disaster recovery services may be applied406 to a dataset (or other entity) that is associated with the user.

Consider an example in which a particular disaster recovery service isdesigned to ensure that the user's dataset has an RPO and RTO of zero,meaning that the dataset must be immediately available with no loss ofdata in the event that a particular storage system that stores thedataset fails or some other form of disaster occurs. In such an exampleand in order to deliver on these RPO/RTO guarantees, one or more dataservices policies that are associated with such a disaster recoveryservice may be applied and enforced. For example, a data services policymay be applied requiring that the dataset be synchronously replicatedacross a predetermined number of storage systems, meaning that a requestto modify the dataset can only be acknowledge as complete when all ofthe storage systems that include a copy of the dataset have modified thedataset in accordance with the request. Stated different, a modifyingoperation (e.g., a write) is either applied to all copies of the datasetthat reside on the storage systems or to none of the copies of thedataset that reside on the storage systems, such that the failure of onestorage system does not prevent a user from accessing the same,up-to-date copy of the dataset.

Readers will appreciate that many other forms of disaster recoveryservices may be offered and implemented in accordance with embodimentsof the present disclosure. For example, such disaster recovery servicesmay be associated with data services policies that not only guaranteethat a disaster can be recovered from in accordance with a particularRPO or RTO requirement, but disaster recovery services may also beassociated with data service policies that are designed to ensure thatthe dataset (or other entity) can be recovered to a predeterminedlocation or system, disaster recovery services may also be associatedwith data service policies that are designed to ensure that the dataset(or other entity) can be recovered at no more than a predeterminedmaximum cost, disaster recovery services may also be associated withdata service policies that are designed to ensure that specific actionsare carried out in response to detecting a disaster (e.g., spinning up aclone of the failed system), and so on. In other embodiments of thepresent disclosure, additional forms of disaster recovery services maybe offered and implemented.

Another example of data services that may be presented 402 to a user,selected by a user (where such a selection is received 404 by one ormore data services modules or similar mechanism), and ultimately applied406 to a dataset associated with the user can include one or more dataarchiving services (including data offloading services). Such dataarchiving services may be embodied, for example, as services that may beprovided to consumers (i.e., a user) of the data archiving services toensure that the user's datasets are archived in a certain way, such asaccording to a certain set of preferences, parameters, and the like. Forexample, one or more data archiving services may be offered to a user toensure that the user's datasets are archived when data has not beenaccessed in a predetermined period of time, when data has been invalidfor a certain period of time, after the dataset reaches a certain size,when a storage system that stores the dataset has reached apredetermined utilization level, and so on. In this example, the dataarchiving services may be presented 402 to a user, a selection of one ormore selected data archiving services may be received 404, and theselected data archiving services may be applied 406 to a dataset that isassociated with the user.

Consider an example in which a particular data archiving service isdesigned to ensure that a portion of a user's dataset that have beeninvalidated (e.g., the portion has been replaced with an updatedportion, the portion has been deleted) are archived within 24 hours ofthe data being invalidated. In order to provide this particular dataarchiving service, the data archiving service may be presented 402 to auser (e.g., via a GUI) and selected by the user, thereby causing aselection of the particular data archiving service to be received 404.In response to receiving 404 the selection of the particular dataarchiving service, one or more data services policies may be applied 406to a dataset associated with the user to carry out the particular dataarchiving service. For example, a data services policy may be appliedrequiring that any operations that would cause data to be invalidated(e.g., an overwrite, a deletion) be cataloged and that every 12 hours aprocess examines the cataloged operations and migrates an invalidateddata to an archive. Likewise, the data services policy may placerestrictions on processes such as garbage collection to prevent suchprocesses from deleting data that has not yet been archived, ifappropriate. Readers will appreciate that in this example, the dataarchiving service may operate in coordination with one or more datacompliance services, as the requirement to archive data may be createdby one or more regulations. For example, a user selecting a particulardata compliance service that requires that certain data be retained fora predetermined period of time may automatically trigger a particulardata archiving service that can deliver the required level of dataarchiving and retention. Likewise, some data archiving services may beincompatible with some data compliance services, such that a user may beprevented from selecting two conflicting or otherwise incompatibleservices.

Another example of data services that may be presented 402 to a user,selected by a user (where such a selection is received 404 by one ormore data services modules or similar mechanism), and ultimately applied406 to a dataset associated with the user can include one or morequality-of-service (‘QoS’) data services. Such QoS data services may beembodied, for example, as services that may be provided to consumers(i.e., a user) of the data compliance services to ensure that the user'sdatasets can be accessed in accordance with predefined performancemetrics. For example, a particular QoS data service may guarantee thatreads that are directed to the user's dataset can be serviced within aparticular amount of time, that writes directed to the user's datasetcan be serviced within a particular amount of time, that a user may beguaranteed a predetermined number of IOPS that are directed to theuser's dataset, and so on. In this example, the QoS data services may bepresented 402 to a user, a selection of one or more selected dataservices may be received 404, and the selected QoS data services may beapplied 406 to a dataset that is associated with the user.

Consider an example in which a particular QoS data service is designedto ensure that a user's datasets can be accessed in a way such that readlatencies and write latencies are guaranteed to be lower than apredetermined amount of time. In order to provide this particular QoSdata service, the QoS data service may be presented 402 to a user (e.g.,via a GUI) and selected by the user, thereby causing a selection of theparticular QoS data service to be received 404. In response to receiving404 the selection of the particular QoS data service, one or more dataservices policies may be applied 406 to a dataset associated with theuser to carry out the particular QoS data service. For example, a dataservices policy may be applied requiring that the dataset be retainedwithin storage that can be used to deliver the required read latenciesand write latencies. For example, if the particular QoS data servicerequired relatively low read latencies and write latencies, the dataservices policies associated with this particular QoS data service mayrequire that the user's dataset be stored in relatively high performancestorage that is located relatively proximate to any hosts that issue I/Ooperations that are directed to the dataset.

Readers will appreciate that many other forms of QoS data services maybe offered and implemented in accordance with embodiments of the presentdisclosure. In fact, the QoS data services may operate in coordinationwith one or more other data services. For example, a particular QoS dataservice may operate in coordination with a particular high availabilitydata service, as the QoS data service may be associated with aparticular availability requirement that can be implemented through theapplication of a particular high availability data service. Likewise, aparticular QoS data service may operate in coordination with aparticular data replication service, as the QoS data service may beassociated with a particular performance guarantee that can only beachieved by replicating (via a particular data replication service) thedataset to storage that is relatively close to the source of I/Ooperations that are directed to the dataset. Likewise, some QoS dataservices may be incompatible with other data services, such that a usermay be prevented from selecting two conflicting or otherwiseincompatible services.

Another example of data services that may be presented 402 to a user,selected by a user (where such a selection is received 404 by one ormore data services modules or similar mechanism), and ultimately applied406 to a dataset associated with the user can include one or more dataprotection services. Such data protection services may be embodied, forexample, as services that may be provided to consumers (i.e., a user) ofthe data protection services to ensure that the user's datasets arebeing protected and disseminated in certain way. For example, one ormore data protection services may be offered to a user to ensure thatthe user's datasets are managed in a way so as to limit how personaldata can be used or disseminated. In this example, the data protectionservices may be presented 402 to a user, a selection of one or moreselected data protection services may be received 404, and the selecteddata protection services may be applied 406 to a dataset that isassociated with the user.

Consider an example in which a particular data protection service isdesigned to ensure that a user's datasets are not disseminated outsideof a particular organization associated with the user. In order toprovide this particular data protection service, the data protectionservice may be presented 402 to a user (e.g., via a GUI) and selected bythe user, thereby causing a selection of the particular data protectionservice to be received 404. In response to receiving 404 the selectionof the particular data protection service, one or more data servicespolicies may be applied 406 to a dataset associated with the user tocarry out the particular data protection service. For example, a dataservices policy may be applied requiring that the dataset not bereplicated, backed up, or otherwise stored in a public cloud such asAmazon AWS™. Likewise, a data services policy may be applied thatrequires that certain credentials be provided in order to access thedataset such as, for example, credentials that can be used to verifythat the requestor is an authorized member of the particularorganization that is associated with the user.

Readers will appreciate that many other forms of data protectionservices may be offered and implemented in accordance with embodimentsof the present disclosure. In fact, the data protection services mayoperate in coordination with one or more other data services. Forexample, a particular data protection service may operate incoordination with one or more data compliance services, as therequirement to restrict the access to or sharing of data may be createdby one or more regulations. For example, a user selecting a particulardata compliance service that requires that certain data cannot be sharedmay automatically trigger a particular data protection service that candeliver the required level of data privacy. Likewise, some dataprotection services may be incompatible with some other data services,such that a user may be prevented from selecting two conflicting orotherwise incompatible services.

Another example of data services that may be presented 402 to a user,selected by a user (where such a selection is received 404 by one ormore data services modules or similar mechanism), and ultimately applied406 to a dataset associated with the user can include one or morevirtualization management services (including container orchestration).Such virtualization management services may be embodied, for example, asservices that may be provided to consumers (i.e., a user) of thevirtualization management services to ensure that the user's virtualizedresources are managed in accordance with predefined policies. Forexample, one or more virtualization management services may be offeredto a user to ensure that the user's datasets can be presented in a waysuch that the dataset is accessible by one or more virtual machines orcontainers associated with the user, even when the underlying datasetresides within storage that may not be typically available to virtualmachines or containers. For example, the virtualization managementservices may be configured to present the dataset as part of a virtualvolume that is made available to a virtual machine, a container, or someother form of virtualized execution environment. In addition, the one ormore virtualization management services may be offered to a user toensure that the user's virtualized execution environments are backed upand can be restored in the event of a failure. For example, thevirtualization management service may cause an image of a virtualmachine to be restored and may capture state information associated withthe virtual machine such that the virtual machine can be restored to itsprevious state if the virtual machine fails. In other embodiments, thevirtualization management services may be used to manage the virtualizedexecution environments associated with the user and to provide storageresources to such virtualized execution environments. In this example,the virtualization management services may be presented 402 to a user, aselection of one or more selected virtualization management services maybe received 404, and the selected virtualization management services maybe applied 406 to a dataset or virtualized environment (includingcombinations thereof) that is associated with the user.

Consider an example in which a particular virtualization managementservice is designed to provide persistent storage to a containerizedapplication that otherwise would not be able to retain data beyond thelife of the container itself, and that the data associated with aparticular container would be retained for 24 hours after the containeris destroyed. In order to provide this particular virtualizationmanagement service, one or more data services policies may be applied406 to a dataset or virtualized environment associated with the user tocarry out the particular virtualization management service. For example,a data services policy may be applied that creates and configures avirtual volume that can be accessed by the container, where the virtualvolume is backed by physical storage on a physical storage system. Insuch an example, once the container is destroyed, the data servicespolicy may also include rules that prevent a garbage collection processor some other process from deleting the contents of the physical storageon a physical storage system that was used to back the virtual volumefor at least 24 hours.

Readers will appreciate that many other forms of virtualizationmanagement services may be offered and implemented in accordance withembodiments of the present disclosure. In fact, the virtualizationmanagement services may operate in coordination with one or more otherdata services. For example, a particular virtualization managementservice may operate in coordination with one or more QoS data services,as the virtualized environments (e.g., virtual machines, containers)that are given access to persistent storage via a virtualizationmanagement service may also have performance demands that can besatisfied through the enforcement of a particular QoS data service. Forexample, a user selecting a particular QoS data service that requiresthat a particular entity receive access to storage resources that canmeet a particular performance requirement may trigger a particularvirtualization management service when the entity is a virtualizedentity. Likewise, some virtualization management services may beincompatible with some other data services, such that a user may beprevented from selecting two conflicting or otherwise incompatibleservices.

Another example of data services that may be presented 402 to a user,selected by a user (where such a selection is received 404 by one ormore data services modules or similar mechanism), and ultimately applied406 to a dataset associated with the user can include one or more fleetmanagement services. Such fleet management services may be embodied, forexample, as services that may be provided to consumers (i.e., a user) ofthe fleet management services to ensure that the user's storageresources (physical, cloud-based, and combinations thereof), and evenrelated resources, are managed in a particular way. For example, one ormore fleet management services may be offered to a user to ensure thatthe user's datasets are distributed across a fleet of storage systems ina way that best suits the performance needs associated with dataset. Forexample, a production version of a dataset may be placed on relativelyhigh performance storage systems to ensure that the dataset can beaccessed using relatively low latency operations (e.g., reads, writes),where a test/dev version of the dataset may be placed on relatively lowperformance storage systems as accessing the dataset using relativelyhigh latency operations (e.g., reads, writes) may be permissible in atest/dev environment. In other examples, the one or more fleetmanagement services may be offered to a user to ensure that the user'sdatasets are distributed in such a way so as to achieve load balancinggoals where some storage systems are not overburdened while others areunderutilized, to ensure that datasets are distributed in such a way soas to achieve high levels of data reduction (e.g., grouping similardatasets together in the hopes of achieving better data deduplicationthat would occur with a random distribution of the datasets), to ensurethat datasets are distributed in such a way so as to adhere to datacompliance regulations, and so on. In this example, the fleet managementservices may be presented 402 to a user, a selection of one or moreselected fleet management services may be received 404, and the selectedfleet management services may be applied 406 to a dataset, storageresource, or other resource that is associated with the user.

Consider an example in which a particular fleet management service isdesigned to ensure that a user's datasets are distributed in such a wayso as to place the datasets on storage systems that are physicallyclosest to the host that most frequently accesses the datasets. In orderto provide this particular fleet management service, one or more dataservices policy may be applied requiring that the location of the hostthat most frequently accesses a particular dataset be taken intoconsideration when placing the dataset. Furthermore, the one or moredata services policy that may be applied may further require that if adifferent host becomes the host that most frequently accesses theparticular dataset, the particular dataset should be replicated to astorage system that is most physically proximate to the different host.

Readers will appreciate that many other forms of fleet managementservices may be offered and implemented in accordance with embodimentsof the present disclosure. In fact, the fleet management services mayoperate in coordination with one or more other data services. Forexample, a particular fleet management service may operate incoordination with one or more data compliance services, as the abilityto move datasets in such a way may be limited by a regulatoryrequirement that is being enforced by one or more data complianceservices. For example, a user selecting a particular data complianceservice that restricts the ability to move a dataset around may cause aparticular fleet management service to only take into considerationtarget storage systems that a dataset could be moved to withoutviolating a policy enforced by a selected data compliance service whenthe fleet management system is evaluating where a dataset that resideson a source storage system should be moved to in pursuit of some fleetmanagement objective. Likewise, some fleet management services may beincompatible with some other data services, such that a user may beprevented from selecting two conflicting or otherwise incompatibleservices.

Another example of data services that may be presented 402 to a user,selected by a user (where such a selection is received 404 by one ormore data services modules or similar mechanism), and ultimately applied406 to a dataset associated with the user can include one or more costoptimization services. Such cost optimization services may be embodied,for example, as services that may be provided to consumers (i.e., auser) of the cost optimization services to ensure that the user'sdatasets, storage systems, and other resources are managed in a way soas to minimize the cost to the user. For example, one or more costoptimization services may be offered to a user to ensure that the user'sdatasets are being replicated in a way that minimizes the costs (e.g.,as measured in terms of dollars) associated with replicating data from asource storage system to any of a plurality of available target storagesystems, to ensure that the user's datasets are being managed in a waythat minimizes the costs associated with storing the dataset, to ensurethat the user's storage systems or other resources are being managed insuch a way so as to reduce the power consumption costs associated withoperation the storage systems or other resources, or in other ways,including ensuring the user's datasets, storage systems, or otherresources are being managed to as to minimize or reduce the cumulativecosts of multiple expenses associated with the datasets, storagesystems, or other resources. In addition, the one or more costoptimization services may even take into consideration contractual costssuch as, for example, a financial penalty associated with violating aservice level agreement associated with a particular user. In fact, thecosts associated with performing an upgrade or performing some otheraction may also be taken into consideration, along with many other formsof costs that can be associated with providing data services and datasolutions to customers. In this example, the cost optimization servicesmay be presented 402 to a user, a selection of one or more selected costoptimization services may be received 404, and the selected costoptimization services may be applied 406 to a dataset that is associatedwith the user.

Consider an example in which a particular cost optimization services isdesigned to ensure that a user's datasets being managed in such a waythat the cost to store the datasets are minimized, in spite of the factthat the user has a separate requirement that the dataset be storedwithin a local, on-premises storage system and also mirrored to at leastone other storage resource. For example, the dataset can be mirrored tothe cloud or mirrored to an off-site storage system. In order to providethis particular cost optimization service, a data services policy may beapplied requiring that, for each possible replication target, the costsassociated with transmitting the dataset to the replication target andthe costs associated with storing the dataset on the replication targetmust be taken into consideration. In such an example, enforcing such adata services policy may result in the dataset being mirrored to thereplication target with the lowest expected costs.

Readers will appreciate that many other forms of cost optimizationservices may be offered and implemented in accordance with embodimentsof the present disclosure. In fact, the cost optimization services mayoperate in coordination with one or more other data services. Forexample, a particular cost optimization service may operate incoordination with one or more QoS data services, as the ability to storea dataset within a particular storage resource may be limited byperformance requirements that are associated with QoS data services. Forexample, a user selecting a particular QoS data service that creates arequirement that the dataset must be accessible within certain latencymaximums, may restrict the ability of the cost optimization service toconsider all possible storage resources as a possible location where thedataset can be stored, as some storage resources (or combination ofstorage resources and other resources such as networking resourcesrequired to access the storage resources) may not be capable ofdelivering the level of performance required by the QoS data service. Assuch, the particular cost optimization service may only take intoconsideration target storage systems that a dataset could reside withinand still be accessed in accordance with the requirements of the QoSdata service that has been selected for the dataset. Likewise, some costoptimization services may be incompatible with some other data services,such that a user may be prevented from selecting two conflicting orotherwise incompatible services.

Another example of data services that may be presented 402 to a user,selected by a user (where such a selection is received 404 by one ormore data services modules or similar mechanism), and ultimately applied406 to a dataset associated with the user can include one or moreworkload placement services. Such workload placement services may beembodied, for example, as services that may be provided to consumers(i.e., a user) of the workload placement services to ensure that theuser's datasets are managed in a way to adhere to various requirementsrelated to where data is stored within a system that includes differentstorage resources. For example, one or more workload placement servicesmay be offered to a user to ensure that the user's datasets are managedin a way so as to load balance accesses to data across the differentstorage resources, to ensure that the user's datasets are managed in away so as to optimize a particular performance metric (e.g., readlatency, write latency, data reduction) for selected datasets, to ensurethat the user's datasets are managed in a way such that mission criticaldatasets are unlikely to be unavailable or subjected to relatively longaccess times, and so on.

Consider an example in which a particular workload placement service isdesigned to achieve a predetermined load balancing objective acrossthree on-premises storage systems that are associated with a particularuser. In order to provide this particular workload placement services, adata services policy may be applied requiring that three storage systemsbe regularly monitored to ensure that each storage system is servicingrelatively similar number of IOPS and also storing a relatively similaramount of data. In such an example, if a first storage system is storinga relatively large amount of data but servicing a relatively smallnumber of IOPS relative to the third storage system, enforcing a dataservices policy associated with the particular workload placementservice may result in moving a relatively large (in terms of GB, forexample) but infrequently accessed dataset on the first storage systemto the third storage system, as well as moving a relatively small (interms of GB) but frequently accessed dataset that is stored on the thirdstorage system to the first storage system—all in pursuit of ensuringthat each storage system is servicing relatively similar number of IOPSand also storing a relatively similar amount of data.

Readers will appreciate that many other forms of workload placementservices may be offered and implemented in accordance with embodimentsof the present disclosure. In fact, the workload placement services mayoperate in coordination with one or more other data services. Forexample, a particular workload placement service may operate incoordination with one or more QoS data services, as the ability to storea dataset within a particular storage resource may be limited byperformance requirements that are associated with QoS data services. Forexample, a user selecting a particular QoS data service that creates arequirement that the dataset must be accessible within certain latencymaximums, may restrict the ability of the workload placement service toload balance across storage resources, as some storage resources (orcombination of storage resources and other resources such as networkingresources required to access the storage resources) may not be capableof delivering the level of performance required by the QoS data service.As such, the particular workload placement service may only take intoconsideration target storage systems that a dataset could reside withinand still be accessed in accordance with the requirements of the QoSdata service that has been selected for the dataset. Likewise, someworkload placement services may be incompatible with some other dataservices, such that a user may be prevented from selecting twoconflicting or otherwise incompatible services.

Another example of data services that may be presented 402 to a user,selected by a user (where such a selection is received 404 by one ormore data services modules or similar mechanism), and ultimately applied406 to a dataset associated with the user can include one or moredynamic scaling services. Such dynamic scaling services may be embodied,for example, as services that may be provided to consumers (i.e., auser) of the dynamic scaling services to ensure scale up and downstorage resources associated with a user's datasets as needed. Forexample, one or more dynamic scaling services may be offered to a userto ensure that the user's datasets and storage resources are managed ina way so as to meet various objectives that can be achieved by scalingmeasures.

Consider an example in which a particular dynamic scaling service isdesigned to ensure that a user's mission critical datasets are managedin a way such that the datasets do not reside on any storage resourcethat is more than 85% utilized in terms of storage capacity or IOPScapacity. In order to provide this particular dynamic scaling service, adata services policy may be applied requiring that: 1) a storageresource be scaled up (if possible) once this utilization threshold ishit, or 2) workloads be rearranged once this threshold is hit in orderto bring the storage resources that store the mission critical datasetbelow 75% in terms of storage capacity and IOPS capacity. For example,if a dataset resides in a cloud-based storage system as described above,the cloud-based storage system may be scaled by adding additionalvirtual drives (i.e., cloud-computing instances with local storage), thecloud-based storage system may be scaled by using higher performancecloud computing instances to execute the storage controllerapplications, and so on. Alternatively, if the dataset resides on aphysical storage system that cannot be immediately scaled, some datasetsmay be migrated off of the physical storage system until utilizationlevels are acceptable.

Readers will appreciate that many other forms of dynamic scalingservices may be offered and implemented in accordance with embodimentsof the present disclosure. In fact, the dynamic scaling services mayoperate in coordination with one or more other data services. Forexample, a particular dynamic scaling service may operate incoordination with one or more QoS data services, as the ability toprovide certain levels of performance as required by the QoS dataservice may be dependent upon having properly scaled storage resources(or other resources). For example, a user selecting a particular QoSdata service that creates a requirement that the dataset must beaccessible within certain latency maximums, may immediately trigger oneor more dynamic scaling services required to scale resources in a waythat the latency targets can be met. Likewise, some dynamic scalingservices may be incompatible with some other data services, such that auser may be prevented from selecting two conflicting or otherwiseincompatible services.

Another example of data services that may be presented 402 to a user,selected by a user (where such a selection is received 404 by one ormore data services modules or similar mechanism), and ultimately applied406 to a dataset associated with the user can include one or moreperformance optimization services. Such performance optimizationservices may be embodied, for example, as services that may be providedto consumers (i.e., a user) of the performance optimization services toensure that the user's datasets, storage resources, and other resourcesare managed in a way to maximize performance as measured by a variety ofpossible metrics. For example, one or more performance optimizationservices may be offered to a user to ensure that the user's storageresources are being maximized in terms of the collective amount of IOPSthat may be service, to ensure that life of different storage resourcesare being maximized through the application of wear leveling policies,by ensuring that the users storage resources are being managed so as tominimize total power consumption, by ensuring that the users storageresources are being managed to ensure uptime of the resources, or inother ways. Likewise, the one or more performance optimization servicesmay be offered to a user to ensure that the user's dataset areaccessible in accordance with various performance objectives. Forexample, the user's datasets may be managed so as to offer the bestperformance in terms of IOPS for particular datasets, the user'sdatasets may be managed so as to offer the best performance in terms ofdata reduction for particular datasets, the user's datasets may bemanaged so as to offer the best performance in terms of availability forparticular datasets, or in some other way.

Consider an example in which a particular performance optimizationservice is designed to ensure that a user's storage resources aremanaged in a way so as to maximize the amount of data that may becollectively stored on the storage resources collectively. In order toprovide this particular performance optimization service, theperformance optimization service may be presented 402 to a user (e.g.,via a GUI) and selected by the user, thereby causing a selection of theparticular performance optimization service to be received 404. Inresponse to receiving 404 the selection of the particular performanceoptimization service, one or more data services policies may be applied406 to a dataset associated with the user to carry out the particularperformance optimization service. For example, a data services policymay be applied requiring that certain types of data be stored on storageresources that implement compression method that are likely to achievethe best data compression results. For example, if one storage systemutilizes compression algorithms that are more effective at compressingtext data and a second storage system utilizes compression algorithmsthat are more effective at compressing video data, implementing the dataservices policies may result in video data being stored on the secondstorage system and text data being stored on the first storage system.Likewise, a data services policy may be applied that requires that datafrom similar host applications be stored in the same storage resourcesin order to improve the level of data deduplication that may beachieved. For example, if data from database application can be moreeffectively deduplicated when deduplicated against data from otherdatabase applications and data from an image processing application canbe more effectively deduplicated when deduplicated against data fromother image processing applications, implementing the data servicespolicies may result in all data from database applications being storedon a first storage system and all data from image processingapplications being stored on a second storage system, in pursuit ofbetter deduplication ratios than would achieve by storing data from eachtype of application on the same storage system. By achieving better datareduction ratios in the backend storage systems, more data can be storedin the storage resources from the perspective of the user.

For example, using the compression example described above, if a firststorage system can compress some data from an uncompressed size of 1 TBto a compressed size of 300 GB whereas a second storage system can onlycompress that same 1 TB of data to a compressed size of 600 GB (becausethe storage systems use different compression algorithms), the amount ofstorage that is available from the user's perspective looks different asstoring the data on the second storage system requires consuming anadditional 300 GB of storage from a backend pool of storage systemswhose physical capacity is fixed (whereas their logical capacity can beimproved through intelligent placement of data).

Readers will appreciate that many other forms of performanceoptimization services may be offered and implemented in accordance withembodiments of the present disclosure. In fact, the performanceoptimization services may operate in coordination with one or more otherdata services. For example, a particular performance optimizationservice may operate in coordination with one or more QoS data services,as the ability to provide certain levels of performance as required bythe QoS data service may restrict the ability to place datasets onparticular storage resources. For example, if a user selects aparticular QoS data service that creates a requirement that the datasetmust be accessible within certain latency maximums but also selects aparticular performance optimization service designed to maximize logicalstorage capacity, only those storage resources that can satisfy bothrequirements may be candidates for receiving the dataset, even if otherstorage resources can provide for better results with respect to oneservice (while not able to meet the requirements of another service).For example, a dataset may not be able to be placed on a particularstorage system that can only offer relatively high I/O latencies(because placing the dataset in such a way would cause the QoS policy tobe violated) even if that particular storage systems may be able toperform excellent data compression of the dataset by virtue ofsupporting a compression algorithm that is highly efficient for thatdataset. As such, some performance optimization services may beincompatible with some other data services, such that a user may beprevented from selecting two conflicting or otherwise incompatibleservices.

Another example of data services that may be presented 402 to a user,selected by a user (where such a selection is received 404 by one ormore data services modules or similar mechanism), and ultimately applied406 to a dataset associated with the user can include one or morenetwork connectivity services. Such network connectivity services may beembodied, for example, as services that may be provided to consumers(i.e., a user) of the network connectivity services to ensure that theuser's datasets, storage resources, networking resources, and otherresources are managed in a way to adhere to various connectivityrequirements. For example, one or more network connectivity services maybe offered to a user to ensure that the user's datasets are reachablevia a predetermined number of networking paths that are not reliant onany shared hardware or software components. Likewise, the one or morenetwork connectivity services may be offered to a user to ensure thatthe user's datasets are managed in a way so as to only be reachable viasecure data communications channels, by ensuring that a user's datasetsare managed in a way so as to inform host applications of the optimaldata communications path for accessing the dataset, to ensure thatstorage resources that store the user's dataset can communicate overdata communications paths that meet certain requirements, and manyothers.

Consider an example in which a particular network connectivity serviceis designed to ensure that a user's datasets are reachable via apredetermined number of networking paths that are not reliant on anyshared hardware or software components. In order to provide thisparticular network connectivity service, the network connectivityservice may be presented 402 to a user (e.g., via a GUI) and selected bythe user, thereby causing a selection of the particular networkconnectivity service to be received 404. In response to receiving 404the selection of the particular network connectivity service, one ormore data services policies may be applied 406 to a dataset associatedwith the user to carry out the particular network connectivity service.For example, a data services policy may be applied requiring that thedataset reside on at least two distinct storage resources (e.g., twodistinct storage systems) that can be reachable from an application hostor other device that accesses the dataset via distinct datacommunications networks. To that end, applying the data services policymay cause the dataset to be replicated from one storage resource toanother, applying the data services policy may cause a mirroringmechanism to be activated to ensure that the dataset resided on bothstorage resources, or some other mechanism may be used to enforce thepolicy.

Readers will appreciate that many other forms of network connectivityservices may be offered and implemented in accordance with embodimentsof the present disclosure. In fact, the network connectivity servicesmay operate in coordination with one or more other data services. Forexample, a particular network connectivity services may operate incoordination with one or more replication services, QoS services, datacompliance services, and other services as the ability to place datasetsin such a way so as to adhere to a particular network connectivityservice may be limited based on the ability to replicate the dataset inaccordance with the replication service, and further based on theability to meet the performance requirements guaranteed by a QoSservice, and still further based on the ability to place datasets inorder to adhere to one or more data compliance services. For example, inthe example set forth above where the network connectivity servicerequired that the dataset reside on at least two distinct storageresources (e.g., two distinct storage systems) that can be reachablefrom an application host or other device that accesses the dataset viadistinct data communications networks, a combination of storageresources could only be selected if the other services could also beprovided. In some embodiments, if two storage resources that could notdeliver on all of the selected services were not available, the usercould be prompted to remove some selected service, a best fit algorithmcould be used to select the two storage systems that came closest tobeing able to deliver the selected services, or some other action couldbe taken. As such, some network connectivity services may beincompatible with some other data services, such that a user may beprevented from selecting two conflicting or otherwise incompatibleservices.

Another example of data services that may be presented 402 to a user,selected by a user (where such a selection is received 404 by one ormore data services modules or similar mechanism), and ultimately applied406 to a dataset associated with the user can include one or more dataanalytic services. Such data analytic services may be embodied, forexample, as services that may be provided to consumers (i.e., a user) ofthe data analytic services to provide data analytics capabilities to auser's datasets, storage resources, and other resources. For example,one or more data analytic services may be offered that analyze thecontents of a user's dataset, that cleanse the contents of the user'sdataset, that perform data collection operations to create or augment auser's datasets, and so on.

Readers will appreciate that many other forms of data analytic servicesmay be offered and implemented in accordance with embodiments of thepresent disclosure. In fact, the data analytic services may operate incoordination with one or more other data services. For example, aparticular data analytic services may operate in coordination with oneor more QoS data services, such that accesses to the dataset for thepurposes of performing data analytics may be given lower priority thanmore traditional accesses of the data (e.g., user-initiated reads andwrites) in order to avoid violating the performance guarantees set forthin the QoS data service. As such, some data analytic services may beincompatible with some other data services, such that a user may beprevented from selecting two conflicting or otherwise incompatibleservices.

Another example of data services that may be presented 402 to a user,selected by a user (where such a selection is received 404 by one ormore data services modules or similar mechanism), and ultimately applied406 to a dataset associated with the user can include one or more dataportability services. Such data portability services may be embodied,for example, as services that may be provided to consumers (i.e., auser) of the data portability services to allow the user to performvarious data movement, data conversion, or similar processes on theuser's datasets. For example, one or more data portability services maybe offered to a user to allow the user to migrate their datasets fromone storage resource to another storage resources, to allow the user toconvert their dataset from one format (e.g., block data) to anotherformat (e.g., object data), to allow the user to consolidate data, toallow the user to transfer their datasets from one data controller(e.g., a first cloud-services vendor) to another data controller (e.g.,a second cloud-services vendor), to allow the users to convert theirdataset from being compliant with a first set of regulations to beingcompliant with a second set of regulations, and so on. In this example,the data portability services may be presented 402 to a user, aselection of one or more selected data portability services may bereceived 404, and the selected data portability services may be applied406 to a dataset that is associated with the user.

Consider an example in which a particular data portability service isdesigned to allow the user to transfer their datasets from a first datacontroller (e.g., a first cloud-services vendor) to a second datacontroller (e.g., a second cloud-services vendor). In order to providethis particular data portability service, a data services policy may beapplied that causes the dataset to periodically be converted to becompatible with the second data controller's infrastructure. Readerswill appreciate that many other forms of data portability services maybe offered and implemented in accordance with embodiments of the presentdisclosure. In fact, the data portability services may operate incoordination with one or more other data services. For example, aparticular data portability service may operate in coordination with oneor more data compliance services, such that migration of a dataset froma first data controller to a second data controller may be restricted soas to not cause the user to violate a regulatory compliance set forth inthe one or more data compliances services. As such, some dataportability services may be incompatible with some other data services,such that a user may be prevented from selecting two conflicting orotherwise incompatible services.

Another example of data services that may be presented 402 to a user,selected by a user (where such a selection is received 404 by one ormore data services modules or similar mechanism), and ultimately applied406 to a dataset associated with the user can include one or moreupgrade management services. Such upgrade management services may beembodied, for example, as services that may be provided to consumers(i.e., a user) of the data compliance services to ensure that the user'sdatasets, storage resources, and other resources can be upgraded or keptup-to-date as various updates become available. For example, one or moreupgrade management services may be offered to a user to ensure that theuser's storage resources are upgrade upon the occurrence of certainthresholds (e.g., age, utilization), to ensure that system software isupgrades as patches and new releases become available, to upgrade cloudcomponents are new cloud service offerings become available, to ensurethat storage related resources such as file systems are upgraded asupgrades or updates become available, and so on.

Consider an example in which a particular upgrade management service isdesigned to ensure that a user's storage resources managed in a way soas to guarantee that new software updates to a user's storage systemsare applied as new software updates become available. In order toprovide this particular upgrade management service, a data servicespolicy may be applied requiring a storage system periodically check forupdates, download any updates, and install updates within 24 hours ofthe update becoming available. Readers will appreciate that many otherforms of upgrade management services may be offered and implemented inaccordance with embodiments of the present disclosure. In fact, theupgrade management services may operate in coordination with one or moreother data services. For example, a particular upgrade managementservice may operate in coordination with one or more QoS services, suchthat updates or upgrades are only applied at times when QoS requirementscan be maintained by the resources being upgraded or by some otherresource. As such, some upgrade management services may be incompatiblewith some other data services, such that a user may be prevented fromselecting two conflicting or otherwise incompatible services.

Another example of data services that may be presented 402 to a user,selected by a user (where such a selection is received 404 by one ormore data services modules or similar mechanism), and ultimately applied406 to a dataset associated with the user can include one or more datasecurity services. Such data security services may be embodied, forexample, as services that may be provided to consumers (i.e., a user) ofthe data security services to ensure that the user's datasets, storageresources, and other resources are managed in a way to adhere to varioussecurity requirements. For example, one or more data security servicesmay be offered to a user to ensure that the user's datasets areencrypted in accordance with certain standards both at rest (when storedon a storage resource) and in transit such that end-to-end encryption isachieved. In fact, the one or more data security services may includeguarantees describing how data will be protected at rest, guaranteesdescribing how data will be protected in transit, guarantees describingprivate/public key systems that will be used, guarantees describing howaccess to the datasets or resources will be restricted, and so on.

Consider an example in which a particular data security service isdesigned to guarantee that a dataset that is stored on a particularstorage resource will be encrypted using keys that are maintained onresources other than the storage resource such as, for example, a keyserver. In order to provide this particular data security service, adata services policy may be applied requiring that the storage resourcerequests a key from the key server, encrypts the dataset (or anyunencrypted portion thereof), and delete the encryption key each timethat the dataset is modified (e.g., via a write). Likewise, in order toservice reads, the storage resource may need to request a key from thekey server, decrypt the dataset, and delete the encryption key. Readerswill appreciate that many other forms of data security services may beoffered and implemented in accordance with embodiments of the presentdisclosure. In fact, the data security services may operate incoordination with one or more other data services. For example, aparticular data security service may operate in coordination with one ormore QoS services, such that only certain QoS services may be madeavailable when a particular data security service is selected, as therequirement to perform various security functions may limit the extentto which high performance guarantees can be made. As such, some datasecurity service may be incompatible with some other data services, suchthat a user may be prevented from selecting two conflicting or otherwiseincompatible services.

Another example of data services that may be presented 402 to a user,selected by a user (where such a selection is received 404 by one ormore data services modules or similar mechanism), and ultimately applied406 to a dataset associated with the user can include one or moreconverged system management services. Such converged system managementservices may be embodied, for example, as services that may be providedto consumers (i.e., a user) of the converged system management servicesto ensure that the user's datasets, storage resources, and otherresources in a converged system are managed in a way to adhere to somepolicies. For example, one or more converged system management servicesmay be offered to a user to ensure that a converged infrastructure thatincludes storage resources and one or more GPU servers that are designedfor AI/ML applications can be managed in a certain way. Likewise, one ormore converged system management services may be offered to a user toensure that a converged infrastructure that includes storage resourcesand on-premises cloud infrastructures (e.g., an Amazon Outpost) may bemanaged in a certain way. For example, the one or more converged systemmanagement services may guarantee that I/O operations that are directedto storage resources and were initiated by the GPU servers in theconverged infrastructure described above will be prioritized over I/Ooperations initiated by devices that are external to the convergedinfrastructure. Readers will appreciate that many other forms ofconverged system management services may be offered and implemented inaccordance with embodiments of the present disclosure. In fact, theconverged system management services may operate in coordination withone or more other data services. As such, some converged systemmanagement services may be incompatible with some other data services,such that a user may be prevented from selecting two conflicting orotherwise incompatible services.

Another example of data services that may be presented 402 to a user,selected by a user (where such a selection is received 404 by one ormore data services modules or similar mechanism), and ultimately applied406 to a dataset associated with the user can include one or moreapplication development services. Such application development servicesmay be embodied, for example, as services that may be provided toconsumers (i.e., a user) of the data compliance services to facilitatethe development and testing of applications, as well as carry out anyother aspects of the application development and testing cycle. Forexample, one or more application development services may be offered toa user to that enable the user to quickly create clones of productiondatasets for development purposes, to create a clone of a productiondataset that has personally identified information obfuscated, to spinup additional virtual machines or containers for testing, to manage allof the connectivity required between test execution environments and thedataset that such environments utilize, and so on. In order to providefor such application development services, one or more data servicespolicies may be applied 406 to a dataset associated with the user tocarry out the particular application development service. For example, adata services policy may be applied that, upon user request, creates aclone of a production dataset, where personally identifiable informationin the dataset obfuscated in the clone, and subsequently stores theclone on a storage resource that is available for development andtesting operations.

Readers will appreciate that many other forms of application developmentservices may be offered and implemented in accordance with embodimentsof the present disclosure. In fact, the application development servicesmay operate in coordination with one or more other data services. Forexample, a particular application development services may operate incoordination with one or more replication policies, such that clones ofa production dataset may only be sent to non-production environments(e.g., to a development and test environment). As such, some applicationdevelopment services may be incompatible with some other data services,such that a user may be prevented from selecting two conflicting orotherwise incompatible services.

Readers will appreciate that while examples were given above in which auser may select multiple services and that compatibility may need to beestablished between the selected services, there are many othercombinations of services (as well as individual services) that may bepresented 402 to a user, selected by a user (where such a selection isreceived 404 by one or more data services modules or similar mechanism),and ultimately applied 406 to a dataset, storage resource, or some otherresource associated with the user. Readers will further appreciate thatmany of the example data services described above (and other services)may include some level of overlap and may also be associated withsimilar, related, or even identical data services policies.

Readers will further appreciate that various mechanisms may be used toattach one or more data services policies to a particular dataset. Forexample, metadata may be attached to the dataset that identifiesparticular data services policies that the dataset is subject to.Alternatively, a centralized repository may be maintained thatassociates identifiers of each dataset with the data services policiesthat the dataset is subjected to. Likewise, various devices may maintaininformation describing datasets that they handle. For example, a storagesystem may maintain information describing data services policies thateach dataset that is stored within the storage system is subjected to,networking equipment may maintain information describing data servicespolicies that each dataset that passes through the networking equipmentis subjected to, and so on. Alternatively, such information may bemaintained elsewhere and may be accessible to the various devices. Inother embodiments, other mechanisms may be used to attach one or moredata services policies to a particular dataset.

For further explanation, FIG. 5 sets forth a flow chart illustrating anadditional example method of providing data management as-a-service inaccordance with some embodiments of the present disclosure. The examplemethod depicted in FIG. 5 is similar to the example method depicted inFIG. 4, as the example method depicted in FIG. 5 also includespresenting 402 one or more available data services to a user, receiving404 a selection of one or more selected data services, and applying 406,in dependence upon the one or more selected data services, one or moredata services policies to a dataset associated with the user.

In the example methods described herein, the one or more available dataservices that are presented 402 to the user may be selected independence upon one or more previously selected services. For exampleand as described in greater detail above, in some situations two dataservices may conflict with each other so that the two data services maynot be delivered at the same time for the same dataset (or same set ofresources). For example, a service that places a storage system in itshighest performance mode may conflict with a service that is intended tominimize the storage resource's power consumption. Likewise, a dataservice that guarantees that a dataset can be accessed with relativelylow read and write latencies may conflict with a data service thatguarantees that writes are mirrored to multiple storage systems beforebeing acknowledged to a host that issues write operations, as it may beimpossible to service writes in a way that is low latency while alsomirroring multiple copies of a write before acknowledging that the writehas completed. As such, an evaluation may be made as to whether aparticular service can actually be delivered given the selections that auser has already made. Continuing with the example above, if a user hasalready selected the service that places a storage system in its highestperformance mode, the service that is intended to minimize the storageresource's power consumption may not be presented 402 to the user—atleast not in a way that allows the user to select the power conservationservice without first de-selecting the high performance service.

The example method depicted in FIG. 5 also includes determining 502whether the selected one or more data services can be applied 406. Inthe example methods described herein, determining 502 whether theselected one or more data services can be applied 406 may be carried outin dependence upon one or more previously selected services. Determining502 whether the selected one or more data services can be applied 406may be carried out, for example, by accessing a list or other source ofinformation describing which services are compatible with each other, byaccessing a list or other source of information describing whichservices are incompatible with each other, or in some other way wherecompatibility is determined based on predetermined configuration orconfiguration-like information. In other embodiments, determining 502whether the selected one or more data services can be applied 406 may bea more dynamic process where information describing the current state ofstorage resources, networking resources, user activity such as theamount of reads and writes being directed to the datasets or storagesystems, and various other metrics may be used to determine 502 whetherthe selected one or more data services can be applied 406.

Consider an example in which a particular service guarantees that adataset can be accessed within relatively low latencies for both readsand writes. In this example, also assume that the storage resources thatstore the dataset are currently operating a full utilization levels,such that the storage resource is servicing as many IOPS as it iscapable of servicing. In such an example, especially where the datasetis not currently able to be accessed within the relatively low latenciesfor both reads and writes that are guaranteed by the particular service,a determination 512 may be made that the particular service cannot beapplied (at least not before other actions such as upgrading the storageresource, migrating workloads, etc.) given that the storage resource isalready operating at its peak performance capacity.

In such an example, if it is affirmatively 504 determined that theselected one or more can be applied, the example method depicted in FIG.5 can proceed by actually applying 506 the one or more selectedservices. If it is determined that the selected one or more cannot 506be applied, however, a user may again be presented 402 one or moreavailable data services. Such a presentation may be modified asdescribed above to preventing conflicting services from being presented,such a presentation may include the user being prompted to de-select oneor more services (with information presented to the user that describesthe incompatibility or conflict), or other modified versions of theservices available may be presented 402.

For further explanation, FIG. 6 sets forth a flow chart illustrating anadditional example method of providing data management as-a-service inaccordance with some embodiments of the present disclosure. The examplemethod depicted in FIG. 6 is similar to the example methods depicted inFIG. 4 and FIG. 5, as the example method depicted in FIG. 6 alsoincludes presenting 402 one or more available data services to a user,receiving 404 a selection of one or more selected data services, andapplying 406, in dependence upon the one or more selected data services,one or more data services policies to a dataset associated with theuser.

In the example methods described herein, the one or more available dataservices that are presented to the user may be selected in dependenceupon one or more permissions associated with the user. The one or morepermissions may represent the extent to which a user is allowed toaccess, modify, or otherwise initiate the application of variousservices. Readers will further appreciate that some services may beassociated with one set of permissions while another set of services maybe associated with a different set of permissions. For example, theability to change which testing and development services are beingapplied may be open to most users whereas the ability to change whichdata compliance services are being applied may be open only to a smallset of trusted users.

In the example method depicted in FIG. 6, presenting 402 one or moreavailable data services to the user can include presenting 602 a costassociated with the one or more available data services to the user.Readers will appreciate that each of the service offerings may beoffered at a price and, as such, pricing information may be presented602 to the user as part of presenting 402 one or more available dataservices to the user. The costs that are presented 602 may be expressed,for example, in terms of an amount of money per amount of time that theservice is applied, an amount of money based on the extent to which theservice is utilized, or in some other way.

In the example method depicted in FIG. 6, in response to receiving 404 aselection of one or more selected data services, the method may includepresenting 604, to the user, an impact on one or more previouslyselected services that would result from applying the one or moreselected data services. The impact on one or more previously selectedservices that would result from applying the one or more selected dataservices may represent the effect on the previously selected servicethat would occur if the one or more selected data services were applied.

Consider an example in which a previously selected service was a servicethat guaranteed that write operations directed to a dataset that wasstored on a first storage system would be serviced within apredetermined, relatively low write latency threshold. In such anexample, assume that a user subsequently selected a service that ensuredthat the dataset could be protected from a failure of the first storagesystem by requiring that the first storage system backup the datacontained in a write operation to a second storage system beforeacknowledging that the write has been completed. In such an example, theaddition of this requirement to essentially have duplicate copies ofdata associated with a write be stored in distinct storage systemsbefore acknowledging that the write has been completed wouldunderstandably result in higher write latencies. As such, presenting 604an impact on one or more previously selected services that would resultfrom applying the one or more selected data services may be carried outby displaying information describing the extent to which the previouslyselected service (i.e., the service that guarantees that writeoperations directed to a dataset that was stored on a first storagesystem would be serviced within a predetermined, relatively low writelatency threshold) would be impacted if the selected data service (i.e.,the service that ensured that the dataset could be protected from afailure of the first storage system by requiring that the first storagesystem backup the data contained in a write operation to a secondstorage system before acknowledging that the write has been completed)were applied. For example, if the average expected write latency wouldincrease from 100 microseconds to 250 microseconds, presenting 604 animpact on one or more previously selected services that would resultfrom applying the one or more selected data services may be carried outby displaying information indicating that the average expected writelatency would increase from 100 microseconds to 250 microseconds. Insuch an example, the impact on one or more previously selected servicesmay be determined by analyzing the performance of similar resources thathad the same or similar services applied, by projecting the impact basedon one or more performance projection formulas, or in some other way.

Readers will appreciate that in the example described in the previousparagraph, the two service may not conflict, such that both services canbe successfully applied at the same time. Stated differently, applyingthe selected data services may not cause the guarantees offered by thepreviously selected data services to be violated. Applying the selecteddata services may, however, impact the previously selected data servicessuch that the user can be presented 604 with the impact on one or morepreviously selected services that would result from applying the one ormore selected data services so that the user can make an informeddecision regarding whether they would like to proceed with applying theselected data services.

In the example method depicted in FIG. 6, applying 406 the one or moredata services policies to the dataset associated with the user caninclude applying 606 the one or more data services policies to a storageresource that contains the dataset. The storage resource that containsthe dataset may be embodied, for example, as one or more of the storagesystems described above including modifications thereof, as one or moreof the cloud-based storage systems described above includingmodifications thereof, as one or more cloud storage resources (e.g.,Amazon S3, Amazon EBS, Azure Blob storage), as one or more storagedevices arranged in some other way, or as combinations of such storageresources. Applying 606 the one or more data services policies to astorage resource that contains the dataset may be carried out, forexample, by modifying the configuration of the storage resources, bymodifying the operation of the storage resources, by monitoring thestorage resources, or in some other way.

Consider an example where a user selects a data service that dynamicallyscales up and down the storage resource that store the user's datasetsin dependence upon a variety of factors such as, for example, the sizeof the dataset, the amount of read and write activity that is directedto the dataset, the extent to which service level agreements associatedwith the datasets are being me, and so on. In such an example, assumethat the storage resource that stores the users dataset is a cloud-basedstorage system as described above, including with reference to FIG. 3C.In such an example, further assume that the dataset is heavily accessedfrom 7 AM until 8 PM, but from 8 PM until 7 AM, the dataset is notaccessed nearly as frequently as the dataset is largely used byemployees of a business organization during business hours. In such anexample, in order to scale up and down the storage resources, relativelypowerful (and expensive) cloud computing instances may be used tosupport the execution of storage controller application in thecloud0based storage system from 7 AM until 8 PM, but the cloud-basedstorage system may be scaled down such that relatively unpowerful (andinexpensive) cloud computing instances may be used to support theexecution of storage controller application in the cloud0based storagesystem from 8 PM until 7 AM. Such an embodiment is one example ofapplying 606 the one or more data services policies to a storageresource that contains the dataset, although many other examples arepossible in accordance with embodiments of the present disclosure.

In the example method depicted in FIG. 6, applying 406 the one or moredata services policies to the dataset associated with the user caninclude applying 608 the one or more data services policies to anetworking resource that transports at least a portion of the dataset.The networking resource that transports at least a portion of thedataset may be embodied, for example, as one or more network switches,as one or more communications fabrics, and so on. Applying 608 the oneor more data services policies to a networking resource that transportsat least a portion of the dataset may be carried out, for example, bymodifying the configuration of the networking resource, by modifying theoperation of the networking resource, by monitoring the networkingresource, or in some other way.

Consider an example in which a user selects a data service that isdesigned to prevent sensitive data from being obtained during transportand later accessed via a malicious actor. In such an example, the dataservice may require that a particular dataset be encrypted when storedor when in transit, and that secure data communications links be usedfor communications between a storage resource that stores the datasetand an application host that accesses the dataset. In such an example,data communications that involve the dataset may be carried usingsecurity protocols such as Transport Layer Security (‘TLS’), usingdedicated communications links that are part of a virtual privatenetwork, or in some other way. In such an example, applying 608 the oneor more data services policies to a networking resource that transportsat least a portion of the dataset may therefore be carried out byconfiguring the networking resources to use a particular securityprotocol, by adding virtual networking resources that may be used tocarry out accesses to the dataset, or in some other way.

Enterprise storage systems, or more particularly a fleet of enterprisestorage systems, typically provide telemetry data to a cloud-basedservice. For example, the telemetry data reported may include datadescribing various operating characteristics of the storage system andmay be analyzed by the cloud-based service for a vast array of purposesincluding, for example, to determine the health of the storage system,to identify workloads that are executing on the storage system, topredict when the storage system will run out of various resources, torecommend configuration changes, workflow migrations, or other actionsthat may improve the operation of the storage system. However, suchcloud-based services do not support a control channel for remote APIrequests directed to storage system APIs or for active management ofsoftware on the storage system. In the following examples, an edgemanagement service provides such a control channel between a cloud-basedservice (i.e., a cloud-based storage service) and an edge device (e.g.,a storage system controller or a server coupled to a storage system).

For further explanation, FIG. 7 sets forth a flow chart illustrating anexample system for non-disruptively moving a storage fleet control planeaccording to some embodiments of the present disclosure. The example ofFIG. 7 includes an edge device 702 that is coupled to a cloud computingenvironment 710. In some examples, the edge device 702 is embodied in aphysical storage system, which may be similar to any of the physicalstorage systems discussed above. Such a storage system may be, forexample, an enterprise data storage system located in an on-premisesdata center. In one example, the edge device 702 may be a storagecontroller of such a storage system. In other examples, the edge device702 may be an enterprise server coupled to an enterprise storage systemor fleet of enterprise storage systems. For example, the edge device 702may be an enterprise server in an enterprise data center that includes afleet of enterprise storage systems. In these examples, the edge device702 may be coupled to the enterprise storage systems through a localnetwork (e.g., a LAN, SAN, etc.). Although, it should not be considereda requirement that the edge device 702 is related to storage systems orstorage services. In fact, the edge device 702 may provide a myriad ofedge services.

Although depicted in less detail, the cloud computing environment 710may be similar to the cloud computing environment 316 described abovewith reference to FIGS. 3A-3C. In fact, the cloud computing environmentmay include the same, fewer, additional components as any of the cloudcomputing environments or cloud-based service environments describedabove. For example, the cloud computing environment may include a publiccloud environment, a private cloud environment, a virtual private cloudenvironment, a hybrid cloud environment, and so on.

In the example of FIG. 7, the control plane 704 is supported by thecloud computing environment 710. In some examples, the control plane 704includes one or more user services 732, 734, 736 executing on cloudresources of the cloud computing environment 710. For example, the cloudresources may be cloud computing instances that are provided by thecloud computing environment 710 to support the execution of software andservices such as the control plane 704. Thus, the control plane 704“resides” in the cloud computing environment 710 in that its executionis supported by cloud resources. In some examples, the user services732, 734, 736 are containerized services or applications whose executionis supported by the cloud computing environment 710. The one or moreuser services 732, 734, 736 of the control plane 704 interface with oneor more agents 706, 716, 726 installed on the edge device 702 to utilizeservices provided by the edge device 702.

In some examples, the user services 732, 734, 736 and the agents 706,716, 726 share an API architecture through which the user services 732,734, 736 may dispatch API calls to agents 706, 716, 726. The agents 706,716, 726, in turn, respond to the API calls by interacting with deviceAPIs 760. The device APIs 760 may be, for example, APIs native to theedge device 702 or APIs that are exposed to the edge device 702 byanother device such as a connected storage system or fleet of storagesystems. In some implementations, the common API architecture utilizedby the user services 732, 734, 736 and the agents 706, 716, 726 is aREST API (e.g., HTTP) or an RPC (e.g., gRPC) API. In some examples, theuser services 732, 734, 736 include API clients 742, 744, 746 while theagents 706, 716, 726 includes API servers 752, 754, 756.

In the example of FIG. 7, the control plane 704 and the agents 706, 716,726 communicate through an edge management service 720 in the cloudcomputing environment 710 and an edge management gateway 722 on the edgedevice 702. The edge management service 720 and edge management gateway722 are coupled by an edge management control channel 712. For example,the edge management control channel 712 may be implemented through anIoT messaging layer based on one or more IoT messaging protocols. Theedge management service 720 may be embodied as a set of computer programinstructions, such as a containerized application stored in cloud-basedstorage, that execute on cloud computing resources. The edge managementgateway 722 may be embodied as a set of computer program instructions,stored on a computer-readable medium, that execute on a processor of theedge device 702.

In these examples, the edge management gateway 722 reads controlmessages from the control plane 704 and communicates with the agents706, 716, 726 and other edge device components (e.g., a containerorchestrator or certificate manager) in response to control messages.Thus, in such examples, the edge management control channel 712 providesa standard messaging layer between the control plane 704 and the agents706, 716, 726 through the edge management service 720 and edgemanagement gateway 722. Further, the edge management control channel 712supports a mechanism for deploying agents 706, 716, 726 to edge device702, which is described in more detail below. In some examples, allmessages passed through the edge management control channel 712 arecryptographically signed in both directions.

In some examples, the edge management control channel 712 includes andIoT messaging layer that implements an IoT messaging protocol such asMQTT. In these examples, the edge management service 720 may receive APIcalls from the API clients 742, 744, 746 of the user services 732, 734,736, where the API calls are directed to API servers 752, 754, 756 ofthe agents 706, 716, 726. The edge management service 720 converts anAPI call to an IoT message by encapsulating the API call in the payloadof an IoT message packet (e.g., an MQTT packet). The edge managementservice 720 then publishes the IoT message to the edge managementcontrol channel 712. The IoT message is read by the edge managementgateway 722, which extracts the API request and forwards it the APIserver 752, 754, 756 of the target agent 706, 716, 726.

For further explanation, FIG. 8 sets forth a diagram of an examplearchitecture for an edge management system that employs a controlchannel between cloud-based storage services and enterprise devices(such as edge servers or enterprise storage systems). The example ofFIG. 8 includes an edge device 802 that is configured to receive controlmessages from a cloud-based storage service 804 over a datacommunication link 806. In some examples, the cloud-based storageservice 804 is a storage service or set of storage services (e.g.,microservices) whose execution is supported by cloud resources (e.g.,cloud computing instances) of a cloud computing environment, such ascloud computing environments discussed above.

In some examples, the edge device 802 is a component of storage systemsuch as any of the storage systems discussed above. For example, edgedevice 802 may be a component of an on-premises storage system locatedin an enterprise data center, a hosted storage system located in aremote or colocation data center, or some other physical storage system.In other examples, the edge device is 802 is an enterprise server orother network appliance configured to interface with one or more storagesystems (e.g., a fleet of storage systems). In these examples, the edgedevice 802 may be, for example, a server that is collocated with one ormore storage systems in an enterprise data center. In yet otherexamples, the edge device 802 is a virtual device such a virtual machinehosted on a physical storage system, server, or other device collocatedwith one or more enterprise storage systems. For ease of explanation, inthe example of FIG. 8, the edge device 802 is described in the contextof a on-premises physical storage system unless otherwise noted. Forexample, the edge device can be embodied in a storage controller 812 ofa storage system 862. The edge device 802 is also configured to sendtelemetry data (e.g., capacity, load, etc.) to the cloud-based storageservice.

In some examples, the cloud-based storage service 804 includes an edgemanagement service 850 for managing the edge device 802 and directingstorage and data services on the edge device 802. In this regard, thecloud-based storage service 804 be associated with a user interface 860for administrators and other enterprise personnel to configure the edgedevice 802 and request storage and data services on the edge device 802.For example, the user interface may be a web-based GUI. In someexamples, the cloud-based storage service 804 includes a cloud-basedcontrol plane 846 for deploying and managing software on the edge device802 as well as providing storage and data services to hosts utilizingthe edge device 802. In some examples, the cloud-based storage service804 includes one or more storage microservices 840, 842, 844 thatprovide storage and data services through a control channel to the edgedevice 802. For example, the storage microservices may include a storageorchestration microservice 842 that autonomously provisions volumes orother datasets on the edge device 802, migrates volumes, scales outstorage, and so on. As another example, the storage microservices mayinclude a data protection as-a-service (DPaaS) microservice 844 thatmanages snapshotting, RPO, RTO, and other data protection policies onthe edge device 802 based on customer-driven objectives. As anotherexample, the microservices may include a software update microservice840 for deploying and upgrading software on the edge device 802. As yetanother example, the storage microservices may include a disasterrecovery as-a-service (DRaaS) microservice that manages data replicationpolicies, failover procedures, and other disaster recovery safeguardsfor the edge device 802. The cloud-based storage service 804 may alsoprovide other storage and data services. In some non-limiting examples,control messages sent to the edge device 802 from the cloud-basedstorage service 804 may include messages directing the edge device 802to install or update software on the edge device 802, provision a volumeon the edge device 802, take a snapshot of a volume on the edge device802, set a policy on the edge device 802 and so on. In some examples,the control plane 846 and its constituent microservices are architectedas a service mesh.

The data communications link 806 may be embodied as a datacommunications pathway that is provided through the use of one or moreprivate and/or public data communications networks such as a LAN, WAN,SAN, the public Internet, a virtual private network, or as some othermechanism capable of transporting digital information between the edgedevice 802 and the cloud-based storage service 804. In such an example,digital information, such as control messages and telemetry data, may beexchanged between the edge device 802 and the cloud-base storage service804 via the data communications link 806 using one or more datacommunications protocols. For example, digital information may beexchanged using hypertext transfer protocol (HTTP), internet protocol(IP), transmission control protocol (TCP), user datagram protocol (UDP),transport layer security (TLS), and so on. In some examples, the datacommunications link 806 utilizes an Internet-of-Things (IoT) messagingprotocol such as the message queue telemetry transport (MQTT), advancedmessage queue protocol (AMQP), or other IoT messaging protocol. In suchexamples, messages and data may be passed between the edge device 802and the cloud-based storage service 804 via a publish/subscribemessaging model and a message broker. In some examples, the IoTmessaging layer supports over-the-air (OTA) installation and update ofsoftware on the edge device 802 from the cloud-based storage service804.

In some implementations, the edge device 802 includes an edge managementgateway 820 configured for communication with the edge managementservice 850 of the cloud-based storage service 804. The edge managementgateway 820 receives control messages published by the edge managementservice 850, including but not limited to software installationmessages, software update messages, security patching, and API calls forstorage orchestration, DPaaS, DRaaS, and other storage and dataservices. As such, the edge management gateway 820 may read controlmessages published by the control plane 846 including the storageorchestration microservice 842, the DPaaS microservice 842, and so on.The edge device 802 also includes one or more storage service agents822, 824, 826 that perform an action based on the control messages. Forexample, the edge device 802 may include a software update agent 822that responds to control messages issued by the control plane 846 basedon a user indication to deploy a software update. Thus, in someexamples, the edge device 802 is configured to receive an “over-the-air”(OTA) software update from the cloud-based storage service 804 via thedata communications link 806. The edge device 802 may also include astorage orchestration agent 824 that responds to control messages issuedby the storage orchestration microservice 842 such as, for example,control messages to create, modify, or move volumes. The edge device 802may also include a DPaaS agent 826 that responds to control messagesissued by the DPaaS microservice 844, such as, for example, controlmessages to set data protection policies, take snapshots, and so on. Itwill be recognized that the edge device 802 may include a variety ofother agents directed to storage and data services that are provided inthe cloud-based storage service 804 and carried out on the edge device802. The edge management gateway 820 routes control messages receivedfrom the cloud-based storage service 804 to the appropriate storageservice agent 822, 824, 826.

In some implementations, the storage service agents 822, 824, 826interface with a storage operating environment 810. In some examples,the storage operating environment 810 is a storage and data servicesapplication that executes on a storage controller 812; however, in otherexamples the storage operating environment 810 may include a set ofcontainerized applications executing on the storage controller 812. Inexamples where the edge device 802 is a storage system, the edge device802 incudes the storage controller 812 and storage resources 814. Thestorage controller 812 and storage resources 814 may be any of thestorage controllers and storage resources discussed above. In exampleswhere the edge device 802 is a separate server or appliance, the storageservice agents 822, 824, 826 interface with a storage operatingenvironment of a storage controller on a storage system that iscollocated with the edge device 802.

In some implementations, the storage service agents 822, 824, 826 areconfigured to invoke storage system APIs exposed by the storageoperating environment 810, a storage system API server, or some otherstorage system component. For example, a storage controller may includea device API server 816 that exposes a library of device APIs 818 forthe storage system. When a control message encapsulating a storage agentAPI request is routed by the edge management gateway 820 to one of thestorage service agents 822, 824, 826, that storage service agentfulfills the request by invoking one or more device APIs 818 exposed bythe device API server 816. In some examples, all messages passed betweenthe edge management gateway 820 and the edge management service 850 arecryptographically signed in both directions.

In some implementations, the edge device 802 includes a containerorchestrator 830 that deploys containerized applications that embody thestorage service agents 822, 824, 826. In some examples, the edgemanagement gateway 820 provides an indication to the containerorchestrator 830 to download an agent package and install a containerimage embodying the storage service agent. For example, auser/administrator may direct the cloud-based storage service 804, viathe user interface, to deploy a particular storage service agent on aparticular edge device. In some examples, the cloud-based storageservice informs the user regarding the permissions that will be grantedto the storage service agent. The cloud-based storage service 804 maypublish a message indicating to the edge management gateway 820 that theparticular storage service agent should be deployed on the edge device802. The edge management gateway 820 may then direct the containerorchestrator 830 to download the agent package, which has been signed bythe cloud-based storage service, and install the agent. In someexamples, the agent package is downloaded from the cloud-based storageservice 804, while in other examples the agent package may be downloadedfrom another trusted source.

In some implementations, the edge device 802 also includes an IoTmessaging interface 832. The IoT messaging interface 832 supports an IoTmessage layer of the data communications link 806 between the edgedevice 802 and the cloud-based storage service 804. The IoT messaginginterface 832 sends and receives messages transmitted in accordance withan IoT messaging protocol such as MQTT or other IoT messaging protocols.For example, the IoT messaging interface 832 may be an AWS Greengrass™Core.

In some implementations, the edge device 802 also includes a cloudconnect manager (CCM) 834. The CCM 834 performs certificate managementas well as log upload to the cloud-based storage service 804. Forexample, the CCM 834 authenticates communication received from thecloud-based storage service 804.

In some implementations, the edge device 802 also includes a proxy 836for communication with the cloud-based storage service 804 and forcommunication among components of the edge device such as the edgemanagement gateway 820, the storage service agents 822, 824, 826, thedevice API server 816, the container orchestrator 830, the IoT messaginginterface 832, the CCM 834, and other components that may not bediscussed here. In some examples, the proxy 836 is an application layer(OSI layer 7) proxy for HTTP message routing. For example, the proxy 836may be an Envoy proxy.

Without loss of generality, consider a non-limiting example where anenterprise administrator installs the edge management gateway 820 on theedge device 802. Once installed, the edge management gateway 820registers the edge device 802 with the edge management service 850.Subsequently, the enterprise administrator requests the edge managementservice 850 to deploy a storage service agent to an enterprise edgedevice 802. In this example, the administrator may initiate an RPC tothe edge management service 850 of the cloud-based storage service 804specifying a storage service agent to deploy and one or more edgedevices on which the storage service agent should be deployed. The edgemanagement service 850 may employ step authentication to verify theidentity of the administrator before deploying the agent. The edgemanagement service 850 publishes a message (e.g., an IoT message) thatis read by the edge management gateway 820 of an edge device 802. Theedge management gateway 820 directs the container orchestrator 830 todownload the agent installation package for that storage service agent.The container orchestrator 830 verifies that the installation package issigned by an authority of the cloud-based storage service 804 and, ifverified, installs the agent on the edge device 802.

Without loss of generality, consider another non-limiting example wherea storage orchestration microservice 842 makes an RPC (e.g., a gRPC) tothe storage orchestration agent 822 to create a new volume. A controlmessage including the RPC is published by the edge management service850 and read by the edge management gateway 820. For example, thecontrol message may be published in an IoT messaging layer that is readthrough IoT messaging interface 832. The edge management gateway 820routes the RPC to the storage orchestration agent 822, which calls adevice API to create a new volume.

For further explanation, FIG. 9 sets forth a flow chart illustrating anexample method of non-disruptively moving a storage fleet control planeaccording to some embodiments of the present disclosure. The examplemethod of FIG. 9 includes deploying 902, on an edge device 901, one ormore agents 905 that are managed by a control plane 903 residing in acloud computing environment 909. Although depicted in less detail, theedge device 901 may be similar to the edge device 702 and the edgedevice 802 described above with reference to FIGS. 7 and 8. In fact, theedge device 901 depicted in FIG. 9 may include the same, fewer,additional components as any of the edge devices described above. Insome embodiments, the edge device 901 is embodied in a physical storagesystem, which may be similar to any of the physical storage systemsdiscussed above. Such a storage system may be, for example, anenterprise data storage system located in an on-premises data center. Inone example, the edge device 901 is a storage controller of such astorage system. In another example, the edge device 901 is an enterpriseserver coupled to an enterprise storage system, for example, in anenterprise or collocation data center.

Although depicted in less detail, the cloud computing environment 909may be similar to the cloud computing environment 316 described abovewith reference to FIGS. 3A-3C or the cloud computing environment 710described above. In fact, the cloud computing environment may includethe same, fewer, additional components as any of the cloud computingenvironments or cloud-based service environments described above. Forexample, the cloud computing environment may include a public cloudenvironment, a private cloud environment, a virtual private cloudenvironment, a hybrid cloud environment, and so on. The control plane903 may be similar to the control planes discussed above with referenceto FIGS. 7 and 8.

In the example of FIG. 9, the control plane 903 is supported by thecloud computing environment 909. In some examples, the control planeincludes a service or set of services executing on cloud resources ofthe cloud computing environment 909. For example, the cloud resourcesmay be cloud computing instances that are provided by the cloudcomputing environment 909 to support the execution of software andservices such as the control plane 903. Thus, the control plane 903“resides” in the cloud computing environment 909 in that its executionis supported by cloud resources. In some examples, the control plane 903includes a set of microservices that are, for example, containerizedservices or applications whose execution is supported by the cloudcomputing environment 909. The control plane 903 manages the edge device901, or a fleet of edge devices 901, through one or more agents 905installed on the edge device 901. For example, the control plane 903 andthe agents 905 share an API architecture through which the control planemakes remote API calls to the agents 905 on the edge device. The agents905, in turn, respond to the API calls by interacting with device APIs.The device APIs may be, for example, APIs native to the edge device 901or APIs that are exposed to the edge device 901 by another device suchas a connected storage system or fleet of storage systems.

In some examples, the control plane 903 is a control plane for a storageservice whose execution is supported by cloud resources, i.e., acloud-based storage service. In these examples, the agents 905 arestorage service agents that are managed by the control plane 903 toprovide storage and data services. In response to API call from thecontrol plane 903 for a storage or data services function, an agent 905invokes one or more storage system APIs to carry out that storage ordata services function. In some examples, the agent 905 may perform atranslation of the API call received by the agent 905 from the controlplane 903 to the storage system APIs that are invoked, particularlywhere the exposed storage system APIs are platform dependent. In variousimplementations, as discussed above, the edge device 901 may be acomponent of a storage system (e.g., a storage controller) or as aserver coupled to a storage system or fleet of storage systems. In oneexample, where the edge device 901 is a storage controller of a storagesystem, the edge device 901 may include one or more storage system APIservers through which the agents 905 invoke storage system APIs. Inanother example, where the edge device 901 is a server, the agents 905may invoke storage system APIs through storage system API serversprovided on connected storage systems.

In some implementations, the control plane 903 includes a set of storagemicroservices that are containerized services deployed on cloudresources, and that manage storage service agents 905 on the edge device901. The storage microservices provide storage and data services thatare carried out on storage systems that are edge devices or that arecoupled to edge devices. For example, the storage microservices mayinclude a storage orchestrator that autonomously provisions volumes,migrates volumes, scales out storage, defines placement groups, andother storage operations or storage-as-code features. As anotherexample, the storage microservices may include a data protectionas-a-service (DPaaS) microservice that manages snapshotting, RPO, RTO,and other data protection policies on storage systems based oncustomer-driven objectives. As another example, the storagemicroservices may include a software update microservice for deployingand upgrading software on storage systems. As yet another example, thestorage microservices may include a disaster recovery as-a-service(DRaaS) microservice that manages data replication policies, failoverprocedures, and other disaster recovery safeguards for storage systems.As still another example, the storage microservices may include a cloudsynchronization microservice that provides AI and ML models and updatesto those modes to storage systems. Readers will appreciate that avariety of other storage and data services may be provided by thecontrol plane 903. In some examples, each agent 905 on an edge devicecorresponds to a microservice of the control plane. As such, there maybe a storage orchestrator agent, DPaaS agent, and so on. These agents905 invoke storage system APIs to carry out storage orchestration, dataprotection, and so on.

In some examples, an edge device 901 and the cloud computing environment909 include nodes of an edge management control channel 911 throughwhich the control plane 903 and the edge device 901 communicate. Forexample, the edge management control channel 911 may be implementedthrough an IoT messaging layer based on one or more IoT messagingprotocols. In one example, the cloud computing environment 909 includesan edge management service 913 that implements a node of the edgemanagement control channel 911. For example, the edge management service913 may be embodied as a set of computer program instructions, such as acontainerized application stored in cloud-based storage, that execute oncloud computing resources. In these examples, the edge managementservice 913 is utilized by the control plane for communication with theagents 905. As another example, the edge device 901 includes an edgemanagement gateway 915 that implements a node of the edge managementcontrol channel 911. For example, the edge management gateway 915 may beembodied as a set of computer program instructions, stored on acomputer-readable medium, that execute on a processor of the edge device901. In these examples, the edge management gateway 915 reads controlmessages from the control plane 903 and communicates with the agents 905and other edge device components (e.g., a container orchestrator orcertificate manager) in response to control messages. Thus, in suchexamples, the edge management control channel 911 provides a standardmessaging layer between the control plane 903 and the agents 905 throughthe edge management nodes. Further, the edge management control channel911 supports a mechanism for deploying agents 905 to edge devices 901,which is described in more detail below.

Deploying 902, on an edge device 901, one or more agents 905 that aremanaged by a control plane 903 residing in a cloud computing environment909 may be carried out by an edge management node 921. In some examples,where the edge management node 921 is the edge management service 913,the edge management service 913 deploys 902 the agents 905 on the edgedevice 901 by sending a control message to the edge management gateway915. For example, the edge management service 913 may receive a request(e.g., through a user interface) from an administrator or otherpersonnel associated with the edge device or fleet of enterprise storagesystems coupled to the edge device. The request may indicate one or moreagents for deployment and one or more edge devices on which the agentsshould be deployed. As one example, where an edge device is implementedin an enterprise storage system, the edge management service 913 mayreceive a request from an administrator of a fleet of enterprise storagesystem identifying a list of storage service agents and a list ofenterprise storage systems on which to deploy those storage serviceagents. In response to such requests, the edge management service 913sends or publishes a control message directed to the edge managementgateway 915 of the identified edge devices. In some implementations, thecontrol message directs the edge management gateway 915 to install anidentified agent 905. In additional implementations, the control messageor subsequent messages may include an installation package for theidentified agent 905. In this manner, the edge management service 913may provide over-the-air (‘OTA’) software for the agent 905. Forexample, an OTA installation package may be a container package for acontainerized application that implements the agent 905. In someimplementations, the edge management service 913 deploys updates andsecurity patches to one or more the agents 905 in the same manner thatthe agents 905 were originally deployed.

In some examples, where the edge management node 921 is the edgemanagement gateway 915, the edge management gateway 915 deploys 902 theagents 905 on the edge device 901 by directing a container orchestratorof the edge device 901 to install an agent 905 identified in the controlmessage received from the edge management service 913. For example, inresponse to a request from the edge management gateway 915, thecontainer orchestrator may download a container package for the agent905 from a container registry associated with the edge managementservice 913 and install an image of an agent application. In suchexamples, the agent 905 may execute on a container platform of the edgedevice. In other implementations, where the agent installation packageis provided by the edge management service 913 as OTA software, the edgemanagement gateway 915 may direct the container orchestrator to installthe agent 905 from the received installation package.

In the example method of FIG. 9, an edge management node 921 alsomediates 904 one or more API requests, generated by the control plane903, directed to the one or more agents 905 on the edge device 901. Insome examples, the edge management service 913 mediates 904 API callsfrom the control plane by receiving an API call that utilizes an APIarchitecture shared by the control plane 903 and the agents 905 andconverting the API call from the control plane 903 into a message thatcan be transmitted over the edge management control channel 911. Forexample, the control plane 903 and agent 905 may utilize a REST (e.g.,HTTP) or RPC (e.g., gRPC) API architecture through which the controlplane 903 invokes functions of the agent 905. For example, where theagent 905 is a storage service agent, the control plane 903 may invoke a‘create volume’ function exposed by an agent API, or a ‘createreplication target’ function exposed by an agent API. In such examples,the edge management service 913 may encapsulate the API call in amessage that can be transmitted over the edge management control channel911 to the edge management gateway 915. In this way, the APIarchitecture of the control plane 903 and the agents 905 is overlayed ona messaging platform utilized by the edge management service 913 andedge management gateway 915 through the edge management control channel911. In some examples, as described in more detail below, the messagingplatform may be an IoT messaging platform, where the messages (i.e.,control messages) are IoT messages in accordance with an IoT messagingprotocol (e.g., MQTT).

In some examples, the edge management gateway 915 mediates 904 API callsby the control plane 903 by receiving a control message from the edgemanagement service 913 that encapsulates the API call by the controlplane 903 and routing the API call to the agent 905. In someimplementations, the edge management gateway 915 extracts the API callfrom the control message. For example, the edge management gateway 915may extract a REST or RPC API call from a message (e.g., an IoT message)transmitted by the edge management service 913. The edge managementgateway 915 then determines, based on an identifier or path indicated inthe API call, to which agent 905 the call is directed and routes thecall to that agent 905. As discussed above, upon receiving the API call,an agent 905 invokes on or more local APIs exposed on the edge device901. For example, an agent 905 may invoke a local platform-dependent APIcall that creates a volume on an enterprise storage system in responseto an API call from the control plane 903.

In the example method of FIG. 9, an edge management node 921 alsomigrates 906, in response to a first condition, the control plane 903 tothe edge device 901. In some examples, the edge management service 913migrates 906 the control plane 903 to the edge device 901 by pushing thecontrol plane 903 to the edge device 901 in response to detecting aparticular condition. For example, the condition may be an impendingscheduled outage where the cloud-based control plane 903 will beunavailable. As another example, the condition may be detecting aconnection quality characteristic such as increased latency, disruptionin connectivity, or other aspects of communication between the edgedevice 901 and the cloud-based control plane 903. For example, the edgemanagement service 913 or the control plane 903 may determine that thetime between a dispatched API call and a response from the agent isabove a predetermined threshold. As another example, the condition maybe a request from an administrator to migrate the control plane 903 tothe edge device 901. In some implementations, the edge managementservice 913 migrates 906 the control plane by directing the edgemanagement gateway 915 (e.g., through a control message) to deploy thecontrol plane 903 or components (e.g., microservices) of the controlplane 903 on the edge device 901.

In some examples, the edge management gateway 915 migrates 906 thecontrol plane 903 to the edge device 901 by deploying the control plane903 in response to a particular condition. For example, the conditionmay be the receipt of a control message from the edge management service913 directing the edge management gateway 915 to deploy the controlplane 903 on the edge device 901. As another example, the condition maybe the detection of a degradation in connectivity to the edge managementservice 913. For example, the edge management gateway 915 may determinethat it is unable to communicate with the edge management service 913and thus the control plane 903. As yet another example, the conditionmay be a request from the administrator to deploy the control plane 903on the edge device. In some implementations, the edge management gateway915 migrates 906 the control plane 903 to the edge device 901 byrequesting the container orchestrator on the edge device 901 to installone or more images for the control plane 903. For example, where thecontrol plane 903 is a collection of microservices, the edge managementgateway 915 may direct the container orchestrator to installcontainerized applications embodying those microservices. In someexamples, the microservices are storage microservices. In some cases,the container orchestrator can download install packages for the controlplane from a container registry associated with the cloud computingenvironment 909. In other cases, the images for the control plane may bealready present on the edge device, in which case the containerorchestrator may launch the container(s) embodying the control plane903. Once the control plane 903 is deployed on the edge device 901, theedge management gateway 915 may confirm successful migration of thecontrol plane 903 to the edge device.

In some implementations, upon successful migration of the control plane903 to the edge device 901, a host connection to the control plane 903is also migrated to the edge device 901. In some examples, the edgemanagement service 913 coordinates the migration of host access to thecontrol plane 903 from the cloud computing environment 909 to the edgedevice 901. For example, the edge management service 913 may redirect avirtual address from the cloud computing environment 909 to a port onthe edge device 901. Thus, host access to the control plane 903 isnon-disruptively migrated from the cloud computing environment 909 tothe edge device 901. Once the control plane 903 has been deployed on theedge device 901, the control plane 903 can dispatch API calls directlyto the agents 905, thus bypassing the edge management service 913 andedge management gateway 915. In other words, no additional encapsulationor adaptation of the API calls is necessary for the agents 905 toreceive those API calls. Because the control plane 903 and the agents905 utilize the same API architecture whether the control plane isresident in the cloud computing environment 909 or on the edge device901 itself, the control plane 903 can remain agnostic to its computingenvironment thus facilitating seamless migration.

For further explanation, FIG. 10 sets forth a flow chart illustratinganother example method of non-disruptively moving a storage fleetcontrol plane according to some embodiments of the present disclosure.The example method of FIG. 10 includes the aspects described above withregard to FIG. 9. However, in the example method of FIG. 10, mediating904 one or more API requests, generated by the control plane 903,directed to the one or more agents 905 on the edge device 901 alsoincludes receiving 1002, by the edge management service 913 associatedwith the control plane 903, an API call directed to one of the agents905, as discussed above. In some implementations, the control plane 903includes one or more API clients. For example, the control plane 903 maycomprise one or more user services (e.g., microservices) where eachservice includes its own API client. The API client may correspond to anAPI server of particular agent 905 on the edge device 901. In oneexample, the API client is a gRPC client. Each of the user services maybe storage microservices such as those discussed above. When amicroservice of the control plane 903 makes an API call to an agent 905through its API client, the API call is mediated by the edge managementservice 913.

In the example of FIG. 10, mediating 904 API requests also includesconverting 1004, by the edge management service 913, an API call to acontrol message that is formatted for a control channel 911 between thecloud computing environment 909 and the edge device 901, as discussedabove. In some implementations, the edge management control channel 911coupling the cloud-based edge management service 913 and the edgemanagement gateway 915 on the edge device 901 is implemented by an IoTmessaging layer utilizing one or more IoT protocols.

In such examples, the edge management service 913 encapsulates the APIcall in an IoT message. For example, the edge management service 913 mayconvert a REST or RPC call directed to an agent to an IoT message thatcan be published over the IoT messaging layer. As one example, the edgemanagement service 913 generates an IoT message packet (e.g., an MQTTpacket) that includes the API call as a payload of the IoT messagepacket.

In the example of FIG. 10, mediating 904 API requests also includesproviding 1006, by the edge management service 913, the control messageto the edge device 901. In some implementations, the edge managementservice 913 provides the control message by publishing an IoT message,that encapsulates the API call, through an IoT messaging layer. The IoTmessage is read by the edge management gateway 915 of the edge device901. For example, the edge management service 913 may publish the IoTmessage to a control channel subscribed to by the edge managementgateway 915. In some examples, the edge management control channel 911includes an MQTT messaging layer, and the IoT message is provided to theedge management gateway 915 as an MQTT message, although it will berecognized that other IoT protocols may be used. It will also berecognized that the edge management service 913 may provide the controlmessage to the edge device through other messaging protocols that arenot IoT protocols.

For further explanation, FIG. 11 sets forth a flow chart illustratinganother example method of non-disruptively moving a storage fleetcontrol plane according to some embodiments of the present disclosure.The example method of FIG. 11 includes the aspects described above withregard to FIG. 9. However, in the example method of FIG. 11, mediating904 one or more API requests, generated by the control plane 903,directed to the one or more agents 905 on the edge device 901 alsoincludes receiving 1102, by an edge management gateway 915 of the edgedevice 901, a control message that encapsulates an API call generated bythe control plane 903. In some examples, as discussed above, the APIcall is encapsulated in a control message that is formatted for the edgemanagement control channel 911. For example, the API call may be a RESTor RPC API call that is encapsulated in an IoT message that is providedover an IoT messaging layer. In some implementations, the edgemanagement gateway 915 is a subscriber of the edge management controlchannel 911, and thus reads the control message subsequent to publishingby the edge management service 913. The edge management gateway 915 isconfigured to identify, from the control message, that the controlmessage includes an API call directed to an agent 905 on the edge device901. For example, the control message may be embodied in a packet (e.g.,and MQTT packet) that indicates the control message includes an API callfor an agent of the edge device.

In the example of FIG. 11, mediating 904 API requests also includestranslating 1104, by the edge management gateway 915 of the edge device901, the control message to the API call. In some examples, the edgemanagement gateway 915 extracts the encapsulated API call generated bythe control plane 903 from the control message that was transmitted bythe edge management service 913. For example, the edge managementgateway 915 may extract the API call from a payload of an IoT messagepacket (e.g., an MQTT packet). In one example, the API call generated bya storage service provided by the control plane 903 is directed to astorage service agent 905 on the edge device 901.

In the example of FIG. 11, mediating 904 API requests also includesrouting 1106, by the edge management gateway 915 of the edge device 901,the API call to an agent 905 on the edge device. In some examples, theedge management gateway 915 forwards the extracted API call to the agent905 to which the API call is directed. For example, the edge managementgateway 915 may include a router for routing agent API calls to agents905. In some implementations, each agent 905 includes an API server,such as an HTTP API server or gRPC API server, that receives the APIcalls generated by the control plane 903. Further, the agent 905 isconfigured to interface with local device APIs, which may be platformdependent. In some examples, the agent 905 is configured to interfacewith an API server of a local storage system. For example, the localstorage system may be an enterprise storage system that includes theedge device or a collocated enterprise storage system. The agent 905,whether the edge device 901 is included in a storage system or as astandalone appliance, may be configured to interface with storage systemAPI servers of a fleet of storage systems. Thus, in someimplementations, the edge device 901 and agents 905 on the edge devicemay be configured to receive API calls generated by the control plane903 and invoke, in response to those API calls, storage system APIs fora local storage system or fleet of storage systems, thus allowingstorage and data services on enterprise storage systems to be managed bya cloud-based control plane 903.

However, subsequent to migration of the control plane 903 to the edgedevice 901, an API call generated by the migrated control plane 903 andreceived by the agent 905 bypasses the edge management service 913 andedge management gateway 915. That is, the migrated control plane 903 candirectly invoke agent APIs without converting the API to and from amessage formatted for the edge management control channel 911. Further,because the control plane 903 and the agent 905 share the same APIarchitecture whether the control plane 903 is cloud-based or local tothe edge device 901, no adaption of the API call is required for the APIcall to the agent. For example, an API client of the control plane 903can directly call on an API server of the agent, thus bypassing the edgemanagement gateway 915. For example, where the control plane includesone or more storage microservices, a containerized storage microservicethat is now deployed on the edge device 901 (after migration) can nowgenerate an API call directed to a storage service agent 905 on the edgedevice 901 without any conversion, translation, or other adaption ofthat API call.

For further explanation, FIG. 12 sets forth a flow chart illustratinganother example method of non-disruptively moving a storage fleetcontrol plane according to some embodiments of the present disclosure.The example method of FIG. 12 includes the aspects described above withregard to FIG. 9. The example of FIG. 12 also includes migrating 1202,in response to a second condition, the control plane 903 from the edgedevice 901 to the cloud computing environment 909. In some examples, theedge management service 913 migrates 1202 the control plane 903 from theedge device 901 to the cloud computing environment 909 by redeployingthe control plane 903 in response to detecting a particular condition.For example, the condition may be the completion of scheduled outagewhere a cloud-based control plane 903 was unavailable. As anotherexample, the condition may be an improvement to latency, connectivity orother aspects of communication between the edge device 901 and the cloudcomputing environment 909. As another example, the condition may be arequest from an administrator to migrate the control plane 903 to thecloud computing environment 909. Upon successful migration of thecontrol plane 903 from the edge device 901 to the cloud computingenvironment 909, a host connection to the control plane 903 is alsomigrated to the cloud computing environment 909. In some examples, theedge management service 913 coordinates the migration of host access tothe control plane 903. For example, the edge management service 913 mayredirect a virtual address from a port on the edge device 901 to a cloudcomputing instance in the cloud computing environment 909. Thus, hostaccess to the control plane 903 is non-disruptively migrated from theedge device 901 to the cloud computing environment 909. Once the controlplane 903 has been deployed in the cloud computing environment 909 theedge management service 913 resumes the mediation of API calls generatedby the control plane and directed to agents 905 on the edge device 901,while host access to the control plane 903 is not disrupted.

Example embodiments are described largely in the context of a fullyfunctional computer system. Readers of skill in the art will recognize,however, that the present disclosure also may be embodied in a computerprogram product disposed upon computer readable storage media for usewith any suitable data processing system. Such computer readable storagemedia may be any storage medium for machine-readable information,including magnetic media, optical media, or other suitable media.Examples of such media include magnetic disks in hard drives ordiskettes, compact disks for optical drives, magnetic tape, and othersas will occur to those of skill in the art. Persons skilled in the artwill immediately recognize that any computer system having suitableprogramming means will be capable of executing the steps of the methodas embodied in a computer program product. Persons skilled in the artwill recognize also that, although some of the example embodimentsdescribed in this specification are oriented to software installed andexecuting on computer hardware, nevertheless, alternative embodimentsimplemented as firmware or as hardware are well within the scope of thepresent disclosure.

Embodiments can include be a system, a method, and/or a computer programproduct. The computer program product may include a computer readablestorage medium (or media) having computer readable program instructionsthereon for causing a processor to carry out aspects of the presentdisclosure.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present disclosure may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present disclosure.

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to some embodimentsof the disclosure. It will be understood that each block of theflowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, can beimplemented by computer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Readers will appreciate that the steps described herein may be carriedout in a variety ways and that no particular ordering is required. Itwill be further understood from the foregoing description thatmodifications and changes may be made in various embodiments of thepresent disclosure without departing from its true spirit. Thedescriptions in this specification are for purposes of illustration onlyand are not to be construed in a limiting sense. The scope of thepresent disclosure is limited only by the language of the followingclaims.

What is claimed is:
 1. A method comprising: deploying, on an edgedevice, one or more agents that are managed by a control plane residingin a cloud computing environment; mediating one or more API requests,generated by the control plane, directed to the one or more agents onthe edge device; and migrating, in response to a first condition, thecontrol plane to the edge device.
 2. The method of claim 1, wherein thecontrol plane includes one or more containerized microservices.
 3. Themethod of claim 1, wherein the edge device is embodied in a storagesystem; wherein the one or more agents are storage service agents; andwherein the control plane provide one or more storage and data servicesutilizing the one or more storage service agents.
 4. The method of claim1, wherein the one or more agents are configured to interface with oneor more local APIs of the edge device.
 5. The method of claim 1, whereinthe control plane and the one or more agents utilize a particular APIarchitecture; and wherein the control plane utilizes the particular APIarchitecture when residing in the cloud computing environment and whenresiding on the edge device subsequent to migration.
 6. The method ofclaim 1, wherein the first condition relates to at least one of anavailability of the control plane, a quality of a data link between thecontrol plane and the one or more agents, and a user request.
 7. Themethod of claim 1, wherein mediating one or more API requests, generatedby the control plane, directed to the one or more agents on the edgedevice includes: receiving, by an edge management service associatedwith the control plane, an API call directed to one of the agents;converting, by the edge management service, the API call to a controlmessage that is formatted for a control channel between the cloudcomputing environment and the edge device; and providing, by the edgemanagement service to the edge device, the control message.
 8. Themethod of claim 7, wherein the control message is embodied in anInternet-of-Things (IoT) message and the control channel includes an IoTmessaging layer.
 9. The method of claim 8, wherein the IoT messageencapsulates the API call.
 10. The method of claim 1, wherein mediatingone or more API requests, generated by the control plane, directed tothe one or more agents on the edge device includes: receiving, by anedge management gateway of the edge device, a control message thatencapsulates an API call generated by the control plane; translating, bythe edge management gateway of the edge device, the control message tothe API call; and routing, by the edge management gateway of the edgedevice, the API call to an agent on the edge device.
 11. The method ofclaim 1, wherein, subsequent to migration of the control plane, an APIcall generated by the migrated control plane and received by an agentbypasses mediation by an edge management node.
 12. The method of claim 1further comprising: migrating, in response to a second condition, thecontrol plane from the edge device to the cloud computing environment.13. An apparatus comprising a computer processor, a computer memoryoperatively coupled to the computer processor, the computer memoryhaving disposed within its computer program instructions that, whenexecuted by the computer processor, cause the apparatus to carry out thesteps of: deploying, on an edge device, one or more agents that aremanaged by a control plane residing in a cloud computing environment;mediating one or more API requests, generated by the control plane,directed to the one or more agents on the edge device; and migrating, inresponse to a first condition, the control plane to the edge device. 14.The apparatus of claim 13, wherein the edge device is embodied in astorage system; and wherein the control plane includes one or morestorage and data services for the storage system.
 15. The apparatus ofclaim 13, wherein the one or more agents are configured to interfacewith one or more local APIs of the edge device.
 16. The apparatus ofclaim 13, wherein mediating one or more API requests, generated by thecontrol plane, directed to the one or more agents on the edge deviceincludes: receiving, by an edge management service associated with thecontrol plane, an API call directed to one of the agents; converting, bythe edge management service, the API call to a control message that isformatted for a control channel between the cloud computing environmentand the edge device; and providing, by the edge management service tothe edge device, the control message.
 17. The apparatus of claim 13,wherein mediating one or more API requests, generated by the controlplane, directed to the one or more agents on the edge device includes:receiving, by an edge management gateway of the edge device, a controlmessage that encapsulates an API call generated by the control plane;translating, by the edge management gateway of the edge device, thecontrol message to the API call; and routing, by the edge managementgateway of the edge device, the API call to an agent on the edge device.18. The apparatus of claim 13, wherein, subsequent to migration of thecontrol plane, an API call generated by the migrated control plane andreceived by an agent bypasses mediation by an edge management node. 19.The apparatus of claim 13 further comprising: migrating, in response toa second condition, the control plane from the edge device to the cloudcomputing environment.
 20. A computer program product for providing datamanagement as-a-service, the computer program product disposed upon acomputer readable medium, the computer program product comprisingcomputer program instructions that, when executed, cause a computer tocarry out the steps of: deploying, on an edge device, one or more agentsthat are managed by a control plane residing in a cloud computingenvironment; mediating one or more API requests, generated by thecontrol plane, directed to the one or more agents on the edge device;and migrating, in response to a first condition, the control plane tothe edge device.